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Sintered parts mechanical properties are very sensitive to final density, which inevitable cause an enormous density gradient in the green part coming from the compaction process strategy. The current experimental method to assess green density occurs mainly in set up by cutting the green parts in pieces and measuring its average density in a balance using Archimedes principle. Simulation is the more accurate method to verify gradient density and the main benefit would be the correlation with the critical region in terms of stresses obtained by FEA and try to pursue the optimization process. This paper shows a case study of a part that had your fatigue limit improved 1000% using compaction process simulation for better optimization.

In recent decades, significant technological advances have made cruise control systems safer, more automated, and available in more driving scenarios. However, comparatively little progress has been made in optimizing vehicle efficiency while in cruise control. In this paper, two distinct strategies are proposed to deliver efficiency benefits in cruise control by leveraging flexibility around the driver’s requested set speed, and road information that is available on-board in many new vehicles. In today’s cruise control systems, substantial energy is wasted by rigidly controlling to a single set speed regardless of the terrain or road conditions. Introducing even a small allowable “error band” around the set speed can allow the propulsion system to operate in a pseudo-steady state manner across most terrain. As long as the vehicle can remain in the allowed speed window, it can maintain a roughly constant load, traveling slower up hills and faster down hills.

After-treatment sensors are used in the ECU feedback control to calibrate the engine operating parameters. Due to their contact with exhaust gases, especially NOx sensors are prone to soot deposition with a consequent decay of their performance. Several phenomena occur at the same time leading to sensor contamination: thermophoresis, unburnt hydrocarbons condensation and eddy diffusion of submicron particles. Conversely, soot combustion and shear forces may act in reducing soot deposition. This study proposes a predictive 3D-CFD model for the analysis of the development of soot deposition layer on the sensor surfaces. Alongside with the implementation of deposit and removal mechanisms, the effects on both thermal properties and shape of the surfaces are taken in account. The latter leads to obtain a more accurate and complete modelling of the phenomenon influencing the sensor overall performance.

Internal combustion engines (ICEs) exhaust emissions, particularly nitrogen oxides (NOx), have become a growing environmental and health concern. The biggest challenge for contemporary ICE industry is the development of clean ICEs, and the use of advanced design tools like Computational Fluid Dynamics (CFD) simulation is paramount to achieve this goal. In particular, the development of aftertreatment systems like Selective Catalyst Reduction (SCR) is a key step to reduce NOx emissions, and accurate and efficient CFD models are essential for its design and optimization. In this work, we propose a novel 3D-CFD methodology, which uses a Machine Learning (ML) approach as a surrogate model for the SCR catalyst chemistry, which aims to enhance accuracy of the simulations with a moderate computational cost. The ML approach is trained on a dataset generated from a set of 1D-CFD simulations of a single channel of an SCR catalyst.

Several predictive equations based on the chemical composition of gasoline have been shown to estimate the particulate emissions of light-duty, internal combustion engine (ICE) powered vehicles and are reviewed in this paper. Improvements to one of them, the PEISimDis equation are detailed herein. The PEISimDis predictive equation was developed by General Motor’s researchers in 2022 based on two laboratory gas chromatography (GC) analyses; Simulated Distillation (SimDis), ASTM D7096 and Detailed Hydrocarbon Analysis (DHA), ASTM D6730. The DHA method is a gas chromatography mass spectroscopy (GC/MS) methodology and provides the detailed speciation of the hundreds of hydrocarbon species within gasoline. A DHA’s aromatic species from carbon group seven through ten plus (C7 – C10+) can be used to calculate a Particulate Evaluation Index (PEI) of a gasoline, however this technique takes many hours to derive because of its long chromatography analysis time.

Regulations have been established for the monitoring and reporting of greenhouse gas (GHG) emissions and fuel consumption from the transport sector. Low carbon fuels combined with new powertrain technologies have the potential to provide significant reductions in GHG emissions while decreasing the dependence on fossil fuel. In this study, a lean-burn ethanol-diesel dual-fuel combustion strategy has been used as means to improve upon the efficiency and emissions of a conventional diesel engine. Experiments have been performed on a 2.0 dm3 single cylinder heavy-duty engine equipped with port fuel injection of ethanol and a high-pressure common rail diesel injection system. Exhaust emissions and fuel consumption have been measured at a constant engine speed of 1200 rpm and various steady-state loads between 0.3 and 2.4 MPa net indicated mean effective pressure (IMEP).

Over the last two decades, engine research has been mainly focused on reducing fuel consumption in view of compliance with stringent homologation targets and customer expectations. As it is well known, the objective of overall engine efficiency optimization can be achieved only through the improvement of each element of the efficiency chain, of which mechanical constitutes one of the two key pillars (together with thermodynamics). In this framework, the friction reduction for each mechanical subsystems has been one of the most important topics of modern Diesel engine development. In particular, the present paper analyzes the lubrication circuit potential as contributor to the mechanical efficiency improvement, by investigating the synergistic impact of oil circuit design, oil viscosity characteristics (including new ultra-low formulations) and thermal management. For this purpose, a combination of theoretical and experimental tools were used.

The introduction of new light-duty vehicle emission limits to comply under real driving conditions (RDE) is pushing the diesel engine manufacturers to identify and improve the technologies and strategies for further emission reduction. The latest technology advancements on the after-treatment systems have permitted to achieve very low emission conformity factors over the RDE, and therefore, the biggest challenge of the diesel engine development is maintaining its competitiveness in the trade-off “CO2-system cost” in comparison to other propulsion systems. In this regard, diesel engines can continue to play an important role, in the short-medium term, to enable cost-effective compliance of CO2-fleet emission targets, either in conventional or hybrid propulsion systems configuration. This is especially true for large-size cars, SUVs and light commercial vehicles.

In recent truck applications, single-piece large-diameter propshafts, in lieu of two-piece propshafts, have become more prevalent to reduce cost and mass. These large-diameter props, however, amplify driveline radiated noise. The challenge presented is to optimize prop shaft modal tuning to achieve acceptable radiated noise levels. Historically, CAE methods and capabilities have not been able to accurately predict propshaft airborne noise making it impossible to cascade subsystem noise requirements needed to achieve desired vehicle level performance. As a result, late and costly changes can be needed to make a given vehicle commercially acceptable for N&V performance prior to launch. This paper will cover the development of a two-step CAE method to predict modal characteristics and airborne noise sensitivities of large-diameter single piece aluminum propshafts fitted with different liner treatments.

Structures with multiple materials have now become one of the perceived necessities for automotive industry to address vehicle design requirements such as light-weight, safety, and cost. The objective of this study is to develop a design methodology for multi-material structures accountable for vehicle crash durability. The heuristic topology synthesis approach of Hybrid Cellular Automaton (HCA) framework is implemented to generate multi-material structures with the constraint on the volume fraction of the final design. The HCA framework is integrated with ordered-SIMP (solid isotropic material with penalization) interpolation, artificial material library, as well as statistical analysis of material distribution data to ensure a smooth transition between multiple practical materials during the topology synthesis.

As conventional vehicle design is adjusted to suit the needs of all-electric, hybrid, and fuel-cell powered vehicles, designers are seeking new methods to improve system-level design and enhance structural efficiency; here, multi-material optimization is suggested as the leading method for developing these novel architectures. Currently, diverse materials such as composites, high strength steels, aluminum and magnesium are all considered candidates for advanced chassis and body structures. By utilizing various combinations and material arrangements, the application of multi-material design has helped designers achieve lightweighting targets while maintaining structural performance requirements. Unlike manual approaches, the multi-material topology optimization (MMTO) methodology and computational tool described in this paper demonstrates a practical approach to obtaining the optimum material selection and distribution of materials within a complex automotive structure.

As automakers continue to develop new lightweight vehicles, the application of multi-material parts, assemblies and systems is needed to enhance overall performance and safety of new and emerging architectures. To achieve these goals conventional material selection and design strategies may be employed, such as standard material performance indices or full-combinatorial substitution studies. While these detailed processes exist, they often succeed at only suggesting one material per component, and cannot consider a clean-slate design; here, multi-material topology optimization (MMTO) is suggested as an effective computational tool for performing large-scale combined multi-material selection and design. Unlike previous manual methods, MMTO provides an efficient method for simultaneously determining material existence and distribution within a predefined design domain from a library of material options.

Simple planetary gears are widely used in automobile industry due to their compact design and high power density. A simple planetary gear set consists of a Sun gear, Ring gear, Planets and Carrier which houses planet gears. Mounting of planet pinions on carrier is through pins which is supported on needle roller bearings. A process called staking is used to assemble the pinion pins on to the carrier. Pinion pins have a staking region which after assembly expands outward into staking groove on the carrier to prevent axial movement of the pins. Design of the groove plays a vital role for the fixation of planet pins and robustness a carrier. Planetary carrier staking grooves are designed to meet pinion pin retention and strength targets.

Planetary gear set is one of the most commonly used gear systems in automotive industry as they cater to high power density requirements. A simple planetary gear set consists of a sun gear, ring gear, planets and carrier which houses planet gears. Efficiency of a transmission is dependent upon performance of gear sets involved in power transfer to a great extent. Structural rigidity of a planetary carrier is critical in a planetary gear set as its deflection may alter the load distribution of gears in mesh causing durability and noise issues. Limited studies exist based on geometrical parameters of a carrier which would help a designer in selecting the dimensions at an early stage. In this study, an end to end automated FEA process based on DOE and optimization in Isight is developed. The method incorporates a workflow allowing for an update of carrier geometry, FE model setup, analysis job submission and post-processing of results.

This paper describes the development of an analytical method to assess and optimize halfshaft joint angles to avoid excessive 3rd halfshaft order vibrations during wide-open-throttle (WOT) and light drive-away events. The objective was to develop a test-correlated analytical model to assess and optimize driveline working angles during the virtual design phase of a vehicle program when packaging tradeoffs are decided. A twelve degree-of-freedom (12DOF) system model was constructed that comprehends halfshaft dynamic angle change, axle torque, powertrain (P/T) mount rate progression and axial forces generated by tripot type constant velocity (CV) joints. Note: “tripot” and “tripod” are alternate nomenclatures for the same type of joint. Simple lumped parameter models have historically been used for P/T mount optimization; however, this paper describes a method for using a lumped parameter model to also optimize driveline working angles.

Powertrain mounting systems design and development involves creating and optimizing a solution using specific mount rates and evaluation over multiple operating conditions. These mount rates become the recommended “nominal” rates in the specifications. The powertrain mounts typically contain natural materials. These properties have variation, resulting in a tolerance around the nominal specification and lead to differences in noise and vibration performance. A powertrain mounting system that is robust to this variation is desired. The design and development process requires evaluation of these mounts, within tolerance, to ensure that the noise and vibration performance is consistently met. During the hardware development of the powertrain mounting system, a library of mounts that include the range of production variation is studied. However, this is time consuming.

In practice, the piston wrist pin is either fixed to the connecting rod or floats between the connecting rod and the piston. The tribological behavior of fixed wrist pins have been studied by several researchers, however there have been few studies done on the floating wrist pin. A new bench rig has been designed and constructed to investigate the tribological behavior between floating pins and pin bore bearings. The experiments were run using both fixed pins and floating pins under the same working conditions. It was found that for fixed pins there was severe damage on the pin bore in a very short time (5 minutes) and material transfer occurs between the wrist pin and pin bore; however, for the floating pin, even after a long testing time (60 minutes) there was minimal surface damage on either the pin bore or wrist pin.

In the area of Human Factors and Usability research a desired output of many studies is identification of what value a specific Design Parameter should be set at to minimize customer dissatisfaction. A Customer Loss Function is a simple way to graphically display the probability customers will be dissatisfied at different levels of a given design parameter, due to a given failure mode. Many design parameters however, have two distinct but related Failure Modes (customer disatisfiers), typically representing two ends of the parameter (i.e. too much/too little; too hot/too cold; too fast/too slow). Each of these Failure modes is represented by its own unique Customer Loss Function. This paper will introduce a technique to combine these two One-Sided Loss Functions into a comprehensive Two Sided Loss Function. The mathematics behind the creation of both one sided and two sided loss functions is based on Binary Logistic Regression [1,2,3] Analysis Techniques.

This paper presents an experimental comparative study between the OCTOGONAL-Gate Silicon-on-Insulator (SOI) nMOSFET (OSM) and the conventional SOI nMOSFET (CSM) considering the same bias conditions and the same gate area (AG), in order to verify the influence of this new MOSFET layout style to handle high current for automotive modules. Analog integrated circuits (ICs) design tends to be considered an art due to a large number of variables and objectives to achieve the product specifications. The designer has to find the right tradeoffs to achieve the desired automotive specification such as low power, low voltage, high speed and high current driver. SOI MOSFET's technology is required to provide the growth of embedded electronics. This growth is driving demand for power-handling devices that are smaller yet still provide high current driver capabilities.

To cope with the increasing number of advanced features (e.g., smart-phone integration and side-blind zone alert.) being deployed in vehicles, automotive manufacturers are designing flexible hardware architectures which can accommodate increasing feature content with as fewer as possible hardware changes so as to keep future costs down. In this paper, we propose a formal and quantitative definition of flexibility, a related methodology and a tool flow aimed at maximizing the flexibility of an automotive hardware architecture with respect to the features that are of greater importance to the designer. We define flexibility as the ability of an architecture to accommodate future changes in features with no changes in hardware (no addition/replacement of processors, buses, or memories). We utilize an optimization framework based on mixed integer linear programming (MILP) which computes the flexibility of the architecture while guaranteeing performance and safety requirements.