Automotive Science and Engineering

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Car following process is time-varying in essence, due to the involvement of human actions. This paper develops an adaptive technique for car following modeling in a traffic flow. The proposed technique includes an online fuzzy neural network (OFNN) which is able to adapt its rule-consequent parameters to the time-varying processes. The proposed OFNN is first trained by an growing binary tree learning algorithm in offline mode, which produces favorable extrapolation performance, and then, is adapted to the stream of car following data, e.g. velocity and acceleration of the target vehicle, using an adaptive least squares estimation. The proposed approach is validated by means of real-world car following data sets. Simulation results confirm the satisfactory performance of the OFNN for adaptive car following modeling application.

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In order to achieve a successful new product, and certainly the successful implementation of a new product into a company, it is necessary to have a structured and documented approach to New Product Development (NPD), therefore providing a clear roadmap for the development of new products.New product development is a multi-stage process. Many different models with a varying number of stages have been proposed in the literature which in this paper are briefing them. This review highlights the NPD Models and process, from concept to consumer, and aim to find the consist gap of different NPD’s models in order for a company to succeed and use New products as a source for Competitive advantage.

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In this study, modeling of a fuel jet which has been injected by high pressure into a low-pressure tank are investigated. Due to the initial conditions and the geometry of this case and similar cases (like CNG injectors in internal combustion engines (ICE)), the barrel shocks and Mach disk are observed. Hence a turbulence and transient flow will be expected with lots of shocks and waves. According to the increasing usage of this type of injectors in ICE, more studies should be conducted to find the most accurate and beneficial models for modeling this phenomenon.

In order to find an accurate and beneficial turbulence model ,in this study, three Reynolds-averaged Navier–Stokes (RANS) turbulence models (SST k-ω, RNG and standard k ) and large eddy simulation (LES) turbulence model were compared by the fuel jet characteristics in three regions (outlet of the nozzle, at Mach disk and at the downstream of the flow). Although the LES model needs more time for each test, the results are more reliable and accurate. On the other hand, RANS turbulence models have lots of errors (more than 10 percent) especially for predicting the characteristics of fuel jet at Mach disk.

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Intake and exhaust manifolds are among the most important parts in engine in which pressure loss phenomena has direct impact on with changing volumetric efficiency. In typical 1D simulation codes, the quantity of pressure loss is proportional to the fluid’s mean velocity by Pressure Loss Coefficient (K_p) value. This important coefficient which has substantial rule in engine simulation is usually determined using constant available values, extracted from complicated experiments (like Miller’s tests) in a specified situation. But these values are credible only in situations according to those tests. Coupling 3D simulations with 1D codes is a common method to gain accurate values of these coefficients but this deals with drastic high simulation costs. To address this problem, a more efficient way is replacing an algebraic relation, extracted from 3D calculations, instead of a constant value in 1D code. It’s obvious that in order to reach accurate coefficients in arbitrary conditions (geometric and flow specifications) determining the best numerical method is mandatory.  In present research, after investigating all 3D simulation aspects, six different selected numerical solutions have been implemented on four different bends in ANSYS Fluent.Results have been validated by comparing loss coefficient values of incompressible fluid (water) with Miller loss coefficient values and method with the most accurate and stable results has been discovered. It was found that all these methods are suitable in general (with less than 5% error in coefficient values) but solutions with structured grid and SST k-ω turbulence modeling represented better stability and accuracy.