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This paper demonstrates the possibilities of using Automotive waste plastic material from “end of life” vehicles (ELVs), in the Footwear Industry to manufacture shoe components. The study establishes the sustainability of the flow of ELVs, from the European Car Parc and identifies and estimates the quantity of plastic materials potentially available for recycling from ELVs. Four potential materials, Acrylonitrile/butadiene/styrene (ABS), Polypropylene (PP), Polypropylene/ethylene/propylene/diene (PP/EPDM) and Polyamide (PA), were identified and three materials (PP, PP/EPDM and ABS) were reprocessed from ELV components and evaluated by the Footwear Industry. As a result, ABS was recommended as an economically, suitable replacement for HIPS, the current material used for manufacturing shoe heel components.

An integrated simulation tool has been developed, which is applicable to a wide range of design issues. A key feature introduced for the first time by this new tool is that it is truly a single code, with identical handling of engine, powertrain, vehicle, hydraulics, electrical, thermal and control elements. Further, it contains multiple levels of engine models, so that the user can select the appropriate level for the time scale of the problem (e.g. real-time operation). One possible example of such a combined simulation is the present study of engine block vibration in the mounts. The simulation involved a fully coupled model of performance, thermodynamics and combustion, with the dynamics of the cranktrain, engine block and the driveline. It demonstrated the effect of combustion irregularity on engine shaking in the mounts.

Countless articles and publications3,4,5 have documented and proven the efficacy, benefits and value of operating within a lean system. Furthermore, there exists common agreement amongst leading organizations successfully implementing a lean system that in order to do so it must take into consideration the entire enterprise, that is, from supplier to customer and everything in between6. One of the core issues this paper addresses is when the optimal time is to train and educate the people who currently have, or will have, influence over the ‘enterprise’.

This paper describes the initial works related to the study of Internal Combustion Engines, as an object of mechanical design, at the Universidad Tecnológica de Pereira. It is reported a concise, complete methodology for simple model of internal combustion engine. The emphasis of the paper is placed on the use of the in-cylinder parameters (pressure and temperature) and inertial loads in the crank-slider mechanism to derive the loads that act on all the components of the crank-slider mechanism as well as the theoretical output torque for a given geometrical structure and inertial properties. These loads can then be used to estimate the preliminary dimensions of engine components in the initial stage of engine development. To obtain the pressure and temperature inside the cylinder, under different operation parameters, such as air fuel ratio and spark angle advance, a Zero dimensional model is applied. The heat transfer from the cylinder and friction are not taken into account.

Vehicle dynamics is a discipline of mechanical engineering that benefited of significant improvements thanks to the progress of computational engineering. Vehicle dynamics engineers are using CAE for the development of a vehicle with MBS and FEA. The concurrent use of these two technologies is a standard in the automotive industry. However the current simulation process is not fully efficient because local geometrical and material nonlinearities are not accurately modeled in classical MBS software. This paper introduces a methodology for vehicle dynamics simulation integrating MBS capabilities in one single nonlinear FEA environment enabling an accurate modeling of nonlinearity in vehicles.

This paper describes the application of statistical techniques known as Design of Experiments (D.O.E.) to efficiently use the results of numerical analysis data in order to improve the configuration of automotive systems. The general framework of these techniques is presented in a format aiming at the design engineer as their end user. Besides, a case study is presented with the purpose of illustrating their practical use. The first step of the case study is to build predictive models for the behaviour of the automotive system being developed by means of the Response Surface Method (RSM), using the proper D.O.E. options. Once these predictive models are available, automatic numerical optimization algorithms are used to improve the responses of interest for given operating conditions. Finally, the automotive systems are robust designed taking into account that the operating conditions vary randomly.

The influence of engine variables on the concentration of oxides of nitrogen present in the exhaust of a multicylinder engine was studied. The concentrations of nitric oxide (NO) were measured with either a mass spectrometer or a non-dispersive infrared analyzer. The NO concentration was low for rich operation (deficient in oxygen) and increased with air-fuel ratio to a peak value at ratios slightly leaner than stoichiometric proportions. A further increase in air-fuel ratio resulted in reduced NO concentrations. Advanced spark timing, decreased manifold vacuum, increased coolant temperature and combustion chamber deposit buildup were also found to increase exhaust NO concentration. These results support either directly or indirectly the hypothesis that exhaust NO concentration is primarily a result of the peak combustion gas temperature and the available oxygen.

The paper focuses and develops issues raised by the SAE paper ‘THE FUTURE OF VEHICLE ELECTRICAL POWER SYSTEMS AND THEIR IMPACT ON SYSTEM DESIGN’ [1] and describes the realisation of a vehicle with a 12 V architecture of flexible configuration and a power management function. The paper describes the methodology, reasoning and mission behind the creation of the vehicle, developed after collaborative exercises in Europe and the USA, and resulting in a joint programme involving a major vehicle manufacturer and a European system supplier. The electrical system is becoming the focus of activity world-wide due to rapid changes in vehicle requirements, in the areas of safety, environmental and functional demands. There are opportunities for:- (a) Improved starting (b) Integrated management of power generation and demand. (c) Higher system integrity (d) Higher efficiency (e) Improvement of the vehicle electrical environment, giving benefits in component cost.

The goal of this project was to investigate how to make driver distraction state classification more efficient by applying selected machine learning techniques to existing datasets. The data set used in this project included both overt driver behavior measures (e.g., lane keeping and headway measures) and indices of internal cognitive processes (e.g., driver situation awareness responses) collected under four distraction conditions, including no-distraction, visual-manual distraction only, cognitive distraction only, and dual distraction conditions. The baseline classification method that we employed was a support vector machine (SVM) to first identify driver states of visual-manual distraction and then to identify any cognitive-related distraction among the visual-manual distraction cases and other non-visual manual distraction cases.

Plug-in Hybrid Electric Vehicles (PHEVs) use electric energy from the grid rather than fuel energy for most short trips, therefore drastically reducing fuel consumption. Different configurations can be used for PHEVs. In this study, the parallel pre-transmission, series, and power-split configurations were compared by using global optimization. The latter allows a fair comparison among different powertrains. Each vehicle was operated optimally to ensure that the results would not be biased by non-optimally tuned or designed controllers. All vehicles were sized to have a similar all-electric range (AER), performance, and towing capacity. Several driving cycles and distances were used. The advantages of each powertrain are discussed.

“Digital Prototype” simulations have been used at DaimlerChrysler to achieve vehicle level NVH objectives. The effectiveness of these simulations to guide the design when faced with vehicle parameter uncertainties is discussed. These uncertainties include, but are not limited to, material properties, material gauges, damping, structural geometry, loads, boundary conditions and weld integrity. Manufacturing and assembly processes introduce variations in the nominal values of these parameters resulting in a scatter of vehicle level NVH simulation responses. An example of a high frequency NVH concern will be studied and modified to arrive at robust design guidance when faced with uncertainty. The validity of a “deterministic digital prototype” simulation model and its relevant role as a “trend predictor” rather than “absolute predictor” tool in guiding the design is also discussed.

Ammonia is one of the most useful compounds that react with NOx selectively on a catalyst, such as V2O5-TiO2, under oxygen containing exhaust gas. However ammonia cannot be stored because of its toxicity for the small power generator in populated areas or for the diesel vehicles. A new concept for NOx reduction in diesel engine using ammonia is introduced. This system is constructed from the hydrogen generator by fuel reformer, the ammonia synthesizer, SCR catalyst for NOx reduction and the gas injection system of reformed gas into the cylinder. Experimental results show that, the SCR catalyst provides a very high rate of NOx reduction, reformed gas injection into cylinder is very effective for particulate reduction. WHEN CONSIDERING INTERNAL COMBUSTION ENGINES of the 1990's the question of how to harmonize the engine with the natural environments is one of the greatest problems. The internal combustion engine changes a substance into energy via its explosive combustion.

A new method for the structural study of long hydraulic cylinders has been developed. The rational analysis, taking cognizance of most known conditions and disturbances, is capable of an iterative type solution by computer. Some examples of its use are given, illustrating the effects of stroke length and mounting position on stresses, deflections, internal bearing loads, and critical axial load.

In today’s Automotive world, there is NO need to advocate “Light weighting”. Government policies for carbon footprint reduction combined with high safety standards are driving OEMs to adopt advanced manufacturing technologies. Steel hot forming is selected as most preferred way to reduce weight as it is easy to adopt and commercially known. It had many advantages compare to conventional cold stamping of standard and high tensile steel. The process consists of heating blank to nearly 1000 °C and quenching it in tool to for martensitic structure. Higher strength up to 2000 MPa can be achieved by this process. There are many examples where part weight is reduced by 15 to 20 % by this method. But Steel hot forming has limitation as specific density of steel is still high. Thus, there is limitation to its weight reduction capability. For further reduction, OEMs have started exploring Aluminium hot forming.

As a typical parameter of the road-vehicle interface, the road friction potential acts an important factor that governs the vehicle motion states under certain maneuvering input, which makes the prior knowledge of maximum road friction capacity crucial to the vehicle stability control systems. Since the direct measure of the road friction potential is expensive for vehicle active safety system, the evaluation of this variable by cost effective method is becoming a hot issue all these years. A ‘wheel slip based’ maximum road friction coefficient estimation method based on a modified Dugoff tire model for distributed drive electric vehicles is proposed in this paper. It aims to evaluate the road friction potential with vehicle and wheel dynamics analyzing by using standard sensors equipped on production vehicle, and fully take the advantage of distributed EV that the wheel drive torque and rolling speed can be obtained accurately.

‘Emotional controlling’ is a very efficient way to realize autonomous behaviour of digital human models by closed loop controls. In particular this is an emotional optimization procedure based upon the ‘Hedonic principle’ and thus following closely the human original. Emotional controlling will be outlined and illustrated by an example demonstrating the specific way force and posture induced discomfort is shaping our movements.