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Showing 4 results for Mashhadi

B. Mashhadi, H. Mousavi, M. Montazeri,
Volume 5, Issue 1 (3-2015)

This paper introduces a technique that relates the coefficients of the Magic Formula tire model to the physical properties of the tire. For this purpose, the tire model is developed by ABAQUS commercial software. The output of this model for the lateral tire force is validated by available tire information and then used to identify the tire force properties. The Magic Formula coefficients are obtained from the validated model by using nonlinear least square curve fitting and Genetic Algorithm techniques. The loading and physical properties of the tire such as the internal pressure, vertical load and tire rim diameter are changed and tire lateral forces for each case are obtained. These values are then used to fit to the magic formula tire model and the coefficients for each case are derived. Results show the existing relationships between the Magic Formula coefficients and the loading and the physical properties of the tire. In order to investigate the effectiveness of the method, different parameter values are selected and the lateral force for each case are obtained by using the estimated coefficients as well as with the simulation and the results of the two methods are shown to be very close. This proves the effectiveness and the accuracy of the proposed method.
A.h. Kakaee, B. Mashhadi, M. Ghajar,
Volume 6, Issue 1 (3-2016)

Nowadays, due to increasing the complexity of IC engines, calibration task becomes more severe and the need to use surrogate models for investigating of the engine behavior arises. Accordingly, many black box modeling approaches have been used in this context among which network based models are of the most powerful approaches thanks to their flexible structures. In this paper four network based modeling methods are used and compared to model the behavior of an IC engine: neural networks model (NN), group method of data handling model (GMDH), a hybrid NN and GMDH model (NN-GMDH), and a GMDH model whose structure is determined by genetic algorithm (Genetic-GMDH). The inputs are engine speed, throttle angle, and intake valve opening and closing timing, and the output is the engine brake torque. Results show that NN has the best prediction capability and Genetic-GMDH model has the most flexible and simplest structure and relatively good prediction ability.


B. Mashhadi, M.a. Vesal, H. Amani,
Volume 7, Issue 3 (9-2017)

This paper presents a force field concept for guiding a vehicle at a high speed maneuver. This method is 
similar to potential field method. In this paper, motion constrains like vehicles velocity, distance to obstacle
and tire conditions and such lane change conditions as zero slop condition and zero lateral acceleration are
discussed. After that, possible equations as vehicles path are investigated. Comparing advantages and
disadvantages of 7th, 11th degree and a few other equations, followed by single mass and bicycle models
lead to an improved method, which is presented in this paper. 
Dr Behrooz Mashhadi, Dr Amirhasan Kakaee, Mr Ahmad Jafari,
Volume 9, Issue 1 (3-2019)

In this research, a high-temperature Rankin cycle (HTRC) with two-stage pumping is presented and investigated. In this cycle, two different pressures and mass flow rates in the HTRC result in two advantages. First, the possibility of direct recovery from the engine block by working fluid of water, which is a low quality waste heat source, is created in a HTRC. Secondly, by doing this, the mean effective temperature of heat addition increases, and hence the efficiency of the Rankin cycle also improves.
The proposed cycle was examined with the thermodynamic model. The results showed that in a HTRC with a two-stage pumping with an increase of 8% in the mean effective temperature of heat addition, the cycle efficiency is slightly improved. Although the operational work obtained from the waste heat recovery from the engine cooling system was insignificant, the effect of the innovation on the recovery from the exhaust was significant. The innovation seems not economical for this low produced energy. However, it should be said that although the effect of the innovation on the increase of the recovery cycle efficiency is low, the changes that must be implemented in the system are also low. 

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