Magnetorheological (MR) dampers have significantly advanced automotive suspension systems by providing adaptable damping characteristics in response to varying road conditions and driving dynamics. This review offers a comprehensive analysis of the evolution and integration of MR dampers in semi-active suspension systems. Semi-active systems present an optimal balance by integrating the simplicity inherent in passive systems with the adaptability characteristic of active systems, while mitigating the substantial energy consumption. The fundamental principles of MR technology, the design of MR dampers, and the diverse control strategies employed to optimize suspension performance were examined. The classical, modern, and intelligent control methods, along with the related research, were emphasized. Based on the above-mentioned methods, the benefits of MR semi-active control were highlighted, while the challenges and future research directions in MR damper technology were also addressed. Through a synthesis of recent research findings and practical applications, this paper underscores the advancements in MR-based semi-active suspension systems and their promising prospects in the automotive industry.

This paper proposed a local active control method for the reduction of road noise inside a vehicle cabin. A multichannel simplified hybrid active noise control (sHANC) system was first developed and applied to the rear left seat of a large sport utility vehicle (SUV). The attenuation capability of the sHANC system was investigated through simulations, using reference signals provided by accelerometers on the suspensions and bodywork of the vehicle and microphones on the floor of cabin, respectively. It was shown that compared to the traditional feedforward system, the sHANC system using either vibrational or acoustical reference signals can produce a significant suppression of the narrowband peak noise between 75 and 80 Hz, but the system lost the control capability in a range of 100–500 Hz when the acoustic signals were used as references. To reduce the practical implementation costs while maintaining excellent reduction performance, a modified simplified hybrid ANC (msHANC) system was further proposed, in which combined vibrational and acoustical signals were used as reference signals. The off-line analyses showed that four reference accelerometers can be substituted by ten microphones without compromising attenuation performance, with 3.7 dBA overall noise reduction being achieved. The effect of delays on the reduction performance of msHANC system was also investigated. The result showed that the msHANC system was more sensitive to the delays compared to the sHANC system if using only vibrational reference signals.

Variational mode decomposition (VMD) is a self-adaptive blind signal separation method that can decompose a signal into several band-limited intrinsic mode functions (BLIMFs). However, the choice of decomposition parameters is still a most concerning problem which has a great impact on the decomposition effectiveness. In this paper, the decomposition priority of multiple BLIMFs and the under- and over-decomposition phenomena of VMD are studied systematically. Our results indicate that the essential reason for these phenomena is that the number and value of the initial frequency points do not match the number of actual BLIMF signals and their center frequencies in the input signal. Based on this conclusion, a novel dichotomy-based VMD method is proposed which can adaptively search the center frequencies of the actual components in the input signal without any priority criteria, avoiding the disadvantage whereby the VMD algorithm artificially selects the decomposition parameter. A multi-component simulated signal is constructed to verify the advantage of the proposed method and compare the decomposition results with several methods based on VMD and empirical mode decomposition. The comparisons show that the dichotomy-based VMD has better decomposition performance, as it not only adaptively searches the real components existing in the mixed simulated signal and filters Gaussian white noise, but also obtains weak signals. The proposed method is then applied to diagnose faults in rotating machinery. The noise and vibration signals of a gasoline engine are decomposed by the proposed method, and the results show that the knocking abnormal noise component can be extracted effectively.

Insertion loss (IL) is a more useful acoustic index than noise reduction and transmission loss for muffler design, but it is harder to predict because the prediction of IL requires knowing the characteristics of the muffler itself and the acoustic source. A muffler with porous material and complex meanflow makes the prediction even more challenging. Although calculation of noise reduction and transmission loss with the transient computational fluid dynamics (CFD) can naturally solve the flow–acoustic interaction problem, the published CFD methods have not been adopted to evaluate the IL of the dissipative muffler with exhaust flow. In this paper, a time-domain simulation method is developed to predict the IL of a complex engine muffler with porous material. The noise reduction and load impedance of the muffler were first calculated using a three-dimensional CFD model, in which the porous material was simulated by the governing equations modified with the material porosity and flow resistivity. The IL was then predicted from the noise reduction, the load impedance, and the engine source extracted from the measured exhaust noise. This developed method explores the application of the three-dimensional CFD method in calculating noise reduction, load impedance, and insertion loss of complex dissipative mufflers with exhaust flow.

Compared to the linear frequency domain method, the transient Computational Fluid Dynamics (CFD) method is more effective when analyzing the effect of complex flow in the muffler on its acoustic characteristics. However, most of the existing CFD methods focus on the calculation of the transmission loss (TL) for the muffler only. The other evaluation parameters, such as noise reduction (NR), are rarely investigated. In this paper, a CFD approach was developed systematically for charactering TL, NR, and transfer matrix (TM) of the muffler in the cases without and with exhaust flow. In the proposed approach, only one mesh model with a correction pipe is needed based on two simulation runs. The non-reflective boundary was used as the termination of the correction pipe in the calculation for the TL, while the reflective pressure boundary was set on the correction pipe end in the calculation for the NR. The TM was then derived from the time-domain pressure and mass velocity obtained in the previous runs for calculating TL and NR. The proposed CFD approach was applied to two simple expansion chambers, and the computational predictions of TL, NR, and TM were validated successfully both with the measured results and the analytical results. In conclusion, the proposed CFD approach is effective for full characterization (TL, NR, and TM) of exhaust mufflers without and with exhaust flow.

The high-speed train (HST) is one of the most important transport tools in China, and the sound quality of its interior noise affects passengers’ comfort. This paper proposes a HST sound quality model. The model combines Mel-scale frequency cepstral coefficients (MFCCs), the most popular spectral-based input parameter in deep learning models, with convolutional neural networks (CNNs) to evaluate the sound quality of HSTs. Meanwhile, two input channels are applied to simulate binaural hearing so that the different sound signals can be processed separately. The binaural MFCC-CNN model achieves an accuracy of 96.2% and outperforms the traditional shallow neural network model because it considers the time-varying characteristics of noise. The MFCC features are capable of capturing the characteristics of noise and improving the accuracy of sound quality evaluations. Besides, the results suggest that the time and level differences in sound signals are important factors affecting sound quality at low annoyance levels. The proposed model is expected to optimize the comfort of the interior acoustic environment of HSTs.