The sound quality (SQ) and sound perception assessments of electric vehicles (EVs) clearly differ from those of conventional internal combustion engine vehicles (ICEVs). Therefore, it is essential to describe and evaluate the SQ of EVs. To evaluate the SQ in EVs, it is necessary to organize evaluators for conducting subjective jury tests, which are time-consuming and labor-intensive. In addition, the evaluation results are subject to the evaluators themselves and other external interferences. With the advancement of machine learning and artificial neural networks (ANNs), this problem can be well solved. This paper outlines a model for SQ estimation in EVs based on a genetic algorithm-optimized back propagation artificial neural network (GA-BP ANN). Moreover, the correlation between the physical-psychoacoustical parameters and the subjective SQ estimations obtained from the jury tests was investigated in this study. It was found that the GA-BP ANN SQ model has many advantages in comparison with the multiple linear regression (MLR) model in terms of precision and generalization. In addition, this method is ready to be applied for rapidly evaluating the SQ in EVs without jury tests, and it can also be of high significance in dealing with the acoustical designs and improvements of EVs in the future.
The interior sound quality (SQ) of pure electric vehicles (PEVs) has become an important consideration for users purchasing vehicles. At present, it is insufficient to take the sound pressure level as the interior acoustics design index of PEVs. Transfer path analysis (TPA) and transfer path synthesis (TPS) that take the SQ of interior noise as the improvement target remains in the preliminary exploration stage. In this paper, objective psychoacoustic parameters of SQ were taken as evaluation indexes of interior PEV noise. A virtual interior SQ synthesis model was designed on the basis of TPA and TPS, which combines experimentation and simulation. The SQ synthesis model demonstrates each noise component contribution in a PEV by new SQ separation technology. First, the interior noise transfer path and noise source of the PEV were determined in a synthesis analysis method of the interior PEV noise. Second, on the basis of the composition mechanism of interior noise and the basic principle of TPA, the excitation signal and transfer function of each interior noise path in the PEV were tested. On the basis of TPS, the interior SQ synthesis model of PEV was then established. Finally, the accuracy of the prediction model was verified in simulation and experimental comparison studies on the psychoacoustic objective parameters of SQ. The SQ objective parameter value of each transfer path was quantified by using contribution analysis. The results are expected to improve the comfort of the interior acoustic environment and enhance the competitiveness of vehicle products. They also provide an effective reference and new ideas for the development of interior SQ in PEVs.
In this paper, a hydraulic model for Safety Injection System (SIS) of M310 reactor is extended. The model is checked and calibrated by test results under test conditions. Based on commissioning test criteria, the system’s maximum and minimum pressure drop coefficients are calibrated according to anti-extrapolation method. Considering modifications of various projects, analysis of 41 flow rate curves under different conditions has been performed using this model. These flow rate curves indicate the relationship between injection flow rates and primary circuit pressures. Accident analysis has taken these curves as input data. Also, the strategy for dealing with accident is established based on these curves. Results of accident analysis show that the design of SIS system can satisfy the safety requirements of M310 reactor. The sensitivity analysis of typical conditions illustrates that injection flow rate will increase as the primary circuit pressure decreases. With the same configuration, the injection flow rate during recirculation phase will be smaller than that during direct injection phase, which is mainly caused by the decrement of suction elevation and the increment of fluid temperature. When low head safety injection pump (LHSI PO) is boosting high head safety injection pump (HHSI PO), if the pressure is relatively high, the injection flow rate will not be improved apparently. If the pressure is relatively low, the boosting is necessary. These conclusions can be the basis for the later optimization design.
With the increasing demand of users for the acoustical comfort of commercial vehicles, the sound quality has become one of the important indicators of comfort evaluation. The research focuses on the objective evaluation method of the subjective perception of the sound quality in commercial vehicle. The interior noises of commercial vehicle with an inline six diesel engine are measured. The five psychoacoustic parameters (loudness, roughness, sharpness, fluctuation strength, tonality and articulation index) are applied to the evaluation and analysis of the interior noises of the commercial vehicle. Using psychoacoustic parameters to evaluate the noises in commercial vehicle, it is of great significance for the analysis and control of the noises in commercial vehicle. The research results provide a theoretical basis for guiding the sound quality design and development of commercial vehicles.
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