Search Results (82)

Objective: Rapid vertical manoeuvres and intermittent vibration in autonomous electric vertical take-off and landing (eVTOL) aircraft can provoke pronounced psychological anxiety in passengers. To address this, we propose a closed-loop adaptive system that integrates an in-ear wearable sensor with dynamic regulation of the cabin microenvironment, enabling real-time monitoring of each passenger’s autonomic state and delivering individualised mitigation through a continuous sense–analyse–intervene–feedback loop. Methods: The system is built around a pair of custom in-ear modules that integrate dual-wavelength photoplethysmography (PPG; 525 nm green and 940 nm infrared), galvanic skin response (GSR), and a six-axis inertial measurement unit (IMU) sampled at 200 Hz. To suppress the 20–80 Hz vibration generated by the distributed electric propulsion system, a compliant silicone damping sleeve attenuates high-frequency components at the hardware level, while a Kalman filter fuses the IMU and PPG streams and an adaptive notch filter removes residual rotor harmonics. The pipeline raises the heart-rate-variability (HRV) signal-to-noise ratio (SNR) to 24.1 dB, with a Pearson correlation of 0.96 against a medical-grade chest strap. A hybrid CNN–LSTM network—two convolutional layers (32 filters each) followed by two LSTM layers (128 hidden units)—predicts impending anxiety from HRV time-domain features (RMSSD, pNN50) and frequency-domain features (LF/HF ratio), triggering intervention 8.2 s in advance on average. According to the predicted anxiety level (mild/moderate/severe), a fuzzy controller modulates transcutaneous auricular vagus nerve stimulation (1–5 mA), the binaural-beat frequency (4–8 Hz, theta band), and the cabin lighting colour temperature (2700–6500 K) in real time. The intervention parameters are continuously refined by SPSA-based stochastic optimisation of the HRV recovery rate (step size 0.01; updated every 30 s). Results: In a randomised controlled experiment conducted in a simulated flight environment (N = 50; aged 22–45 years; 1:1 sex ratio), the active group reached physiological recovery in 52.3 s on average, compared with 98.6 s for the sham-controlled group—a 47% reduction (Cohen’s d = 1.24, p < 0.001). User acceptance reached 94%. Conclusions: The proposed in-ear platform enables closed-loop adaptive regulation of anxiety in the eVTOL cabin and overcomes the limitations of conventional passive mitigation strategies. By combining vibration-tolerant physiological sensing with multimodal environmental control, the work offers a practical pathway for improving passenger experience in urban air mobility and provides a useful reference for human-factors standards governing autonomous aircraft. Full article

The present study conducts numerical simulations to investigate the excitation force characteristics of a pre-swirl stator–propeller–rudder system and analyzes the potential benefits of the combined pre-swirl stator and rudder bulb for vibration and noise based on force and pressure fluctuations. The propeller bearing force, rudder force and hull surface pressure are compared and analyzed under conditions with and without energy-saving devices. The results show that the pre-swirl stator and rudder bulb intensify the axial load pulsation of the propeller, which may affect the service life of the main engine and gearbox. The overall level of lateral load pulsation is also increased, which may lead to higher cabin noise. The load pulsation level of the pre-swirl stator is comparable to that of the propeller bearing force, while the increased vibration of the rudder may result in more complex structural safety and noise issues. The reduction in hull surface pressure fluctuation contributes to the mitigation of the low-frequency underwater radiated noise. The influence mechanism of the pre-swirl stator–rudder bulb on the excitation force is of great significance to the ship engineering design. Full article

The open fan as a highly efficient propulsion concept is a promising approach to reduce climate-damaging emissions in aviation. However, the increased vibroacoustic emissions of the fan resulting from the open design lead to elevated cabin noise. Energy harvesting resonators can be used to leverage the piezoelectric effect and to attenuate structural vibrations caused by the acoustic loading simultaneously. To evaluate the potential of a specific configuration of energy harvesting resonators, an investigation of the dynamic interaction between the airframe and the resonators is necessary. Therefore, the eigenmodes and eigenfrequencies of a representative stiffened plate are determined experimentally using modal analysis via laser scanning vibrometry. A finite element model of the stiffened plate with the resonator idealized as a mass–spring element is implemented. The stiffness of this simplified resonator model is calibrated by correlating simulated with experimental results following a model updating approach. Finally, an optimization framework designed to determine the optimal quantity and placement of resonators using the experimentally validated model and representative loads is implemented to maximize both vibroacoustic attenuation and energy harvesting efficiency. The resulting framework serves as a generalized optimization tool capable of systematically optimizing the resonator configuration based on airframe geometry and specified vibroacoustic loading scenarios. Full article

Sound field reproduction (SFR) is vital for noise simulation and acoustic comfort optimization in vehicle cabins. This paper reviews three core SFR techniques: Wave Field Synthesis (WFS), Higher-Order Ambisonics (HOA), and Pressure Matching (PM). Their theoretical fundamentals, engineering optimizations, and adaptability to narrow enclosed cabins are analyzed. We compare the three methods in terms of reproduction accuracy, system complexity, and cost. Key challenges in vehicular applications are summarized, including strong reverberation, multi-source coupling, and the mismatch between physical reproduction and subjective perception. Future directions are proposed, such as physics-data hybrid optimization, low-cost lightweight design, and personalized acoustic comfort. This review offers a practical reference for the engineering application of SFR in vehicle cabin acoustic optimization. Full article

The increasing risk of air pollution in closed areas like passenger vehicles requires smart and real-time air quality reading solutions. Gases such as carbon monoxide (CO)—which is colorless and odorless and is produced by exhaust systems—air conditioners, and combustion sources are very dangerous to health because they can cause respiratory distress and poisoning at high levels. Traditional in-vehicle CO monitoring systems use a single-point sensor and a fixed threshold, which are insufficient in a dynamic cabin environment subject to factors such as vehicle size, ventilation rate, number of occupants, and incoming traffic. To address these drawbacks, this paper proposes a new E-Nose system with Virtual Sensor-Augmented Embedded Intelligence to estimate the CO concentration in vehicle cabins in real time. The system combines data from cheap gas sensors and improves it using virtual sensor machine learning models trained to predict or enhance sensor responses in real time. Embedded intelligence, deployed locally on edge hardware, supports low-latency processing, dynamic calibration, and noise filtering to respond to fluctuating environmental conditions adaptively. This architecture enables more accurate, robust, and context-aware estimation of CO levels compared to traditional threshold-based methods. Experimental validation across varied vehicular scenarios demonstrates superior precision and responsiveness, providing timely warnings even under complex dispersion patterns. Classifier Gradient Boosting, which builds an ensemble of weak learners sequentially, matched the Random Forest with 99.94% training and 98.59% model accuracy, confirming its strong predictive capability. The system is designed to be cost-effective, scalable, and easily integrable into modern automotive platforms. This study also contributes to the field of smart ecological recording and demonstrates the effectiveness of the virtual sensor-enhanced embedded system as an effective way to improve passenger safety by providing pre-emptive on-board air quality monitoring. Full article

The evaluation of vibro-acoustic comfort in vehicle cabins is frequently limited by fragmented treatment of noise and vibration indicators and by the absence of structured, reproducible assessment frameworks. This study proposes an advanced, correlation-driven and process-oriented methodology for vibro-acoustic comfort evaluation, designed to support systematic analysis and decision-making across varying vehicle operating conditions. The proposed framework is formulated as a sequential process comprising experimental data acquisition, signal preprocessing, statistical correlation analysis, and decision-oriented interpretation. The framework was experimentally validated on five special-purpose armored platforms under both stationary and dynamic operating regimes, with repeated measurement trials to ensure robustness. Interior and exterior sound pressure levels, together with vibration-related parameters, are experimentally measured under stationary and dynamic operating regimes. Pearson correlation coefficients are employed to quantify interdependencies among vibro-acoustic variables and identify dominant contributors affecting comfort-related conditions. The results indicate statistically significant correlations between interior noise levels and selected vibration indicators, revealing distinct correlation patterns associated with different operating states. Based on these findings, correlation strength was classified as weak (|r| < 0.3), moderate (0.3 ≤ |r| < 0.6), and strong (|r| ≥ 0.6), enabling structured contributor ranking. The primary contribution of this work consists in elevating correlation analysis from a descriptive statistical technique to a formalized assessment process suitable for integration into predictive modeling and optimization workflows. The framework provides a transferable methodological structure, validated within the investigated vehicle category. Full article

Applications in enclosed spaces such as vehicle cabin on-site detection, human–pet separation, and pet care have put forward higher requirements for non-contact target recognition. Millimeter-wave radar point clouds have advantages such as privacy friendliness and robustness against low light and occlusion. However, their point clouds are generally sparse, with obvious noise and multipath interference. Moreover, the fluctuation of point numbers over time makes alignment and feature learning difficult, which leads to performance degradation of existing point cloud classification methods in complex environments. To this end, this paper proposes a spatiotemporal joint classification framework for millimeter-wave point cloud sequences: An effective point mask mechanism is introduced in the spatial dimension to suppress the interference of invalid points generated by alignment on the neighborhood composition and feature aggregation and improve the reliability of local geometric representation; and to integrate attention-based time series modeling in the time dimension and enhance category separability by using cross-frame dynamic patterns. The experimental results show that the proposed method can achieve an accuracy rate of 97.8% in the three-classification tasks of Child, Cat and Dog and the ablation analysis verifies the key contributions of the mask mechanism and time series modeling to robust recognition. This framework provides a deployable and more generalized millimeter-wave point cloud solution for the identification of life forms in confined spaces. Full article

To address the low-speed starting noise in a small electric vehicle, this study proposes and validates a systematic diagnostic and optimization methodology. A novel objective testing method, based on energy tracking and matching, is first employed for precise noise source localization. Combined with electromagnetic force wave analysis, this method identifies the coupling between a 24th-order motor excitation and a powertrain structural mode as the root cause. Subsequently, a low-cost, integrated optimization scheme is presented, which synergistically combines three strategies: motor control refinement, powertrain natural frequency tuning, and mount isolation enhancement. Experimental validation demonstrates that this multi-domain approach reduces the sound pressure level at the driver’s ear by 4–6 dB(A), effectively eliminating the abnormal audible noise during starting and significantly improving the in-cabin sound quality. This paper offers a cost-effective engineering framework for resolving low-speed, low-frequency noise problems in electric vehicles. Full article

As a highly integrated and compact power unit, the motor-pump finds critical applications in emerging electric vehicle (EV) domains such as electro-hydraulic braking and steering systems, where its vibration and noise performance directly impacts cabin comfort. A key factor limiting its NVH (Noise, Vibration, and Harshness) performance is the electromagnetic vibration and noise induced by the cogging torque of the built-in brushless DC motor (BLDCM). Traditional suppression methods that rely on stator auxiliary slots exhibit certain limitations. To address this issue, this paper proposes a collaborative optimization method integrating multi-parameter scanning and response surface methodology (RSM) for the design of auxiliary slots on the motor-pump’s stator teeth. The approach begins with a multi-parameter scanning phase to identify a promising region for global optimization. Subsequently, an accurate RSM-based prediction model is established to enable refined parameter tuning. Results demonstrate that the optimized stator structure achieves a 91.2% reduction in cogging torque amplitude for the motor-pump. Furthermore, this structure effectively suppresses radial electromagnetic force, leading to a 5.1% decrease in the overall sound pressure level. This work provides a valuable theoretical foundation and a systematic design methodology for cogging torque mitigation and low-noise design in motor-pumps. Full article

This study presents a rigorous experimental investigation of in-cabin acoustic comfort across a heterogeneous set of road and special-purpose vehicles. Interior noise measurements were conducted on a total of 35 vehicles, comprising five vehicles from each of seven operational categories, grouped according to RNTR-2 regulations into three distinct vehicle classes: N1, N2, and N2G. The adopted research methodology ensures a unified, phenomenological, and experimental approach to the assessment of interior vehicle acoustics, enabling consistent data acquisition and comparative analysis across vehicle classes. Measurements were performed under both stationary and dynamic operating conditions using Class 1 precision instrumentation. The experimental results reveal systematic differences in acoustic performance between vehicle classes. While N1 and N2 vehicles generally comply with recommended comfort thresholds, N2G special-purpose vehicles exhibit significantly elevated interior noise levels, reaching up to 90 dBA during dynamic operation, together with increased variability at higher engine regimes. These findings highlight the influence of vehicle architecture, operational conditions, and mission-oriented design constraints on vibro-acoustic behavior. Passive noise control solutions based on advanced sound-absorbing and sound-insulating materials were further evaluated, demonstrating interior noise reductions of up to 10 dBA. The scientific contribution of this work lies in the establishment of a unified, reproducible methodology that enables direct cross-category comparison of in-cabin acoustic comfort while explicitly integrating special-purpose vehicles into a comfort-oriented analytical paradigm. By moving beyond regulatory compliance toward a multidimensional interpretation of acoustic comfort, the study provides a robust foundation for vehicle design optimization and supports the future development of dedicated comfort assessment standards. Full article

This research is based on the acoustic package design of new energy vehicles, investigating the application of the hybrid Finite Element–Statistical Energy Analysis (FE-SEA) model in predicting the high-frequency dynamic response of automotive structures, with a focus on the modeling and correction methods for hybrid point connections. New energy vehicles face unique acoustic challenges due to the special nature of their power systems and operating conditions, such as high-frequency noise from electric motors and electronic devices, wind noise, and road noise at low speeds, which directly affect the vehicle’s ride comfort. Therefore, optimizing the acoustic package design of new energy vehicles to reduce in-cabin noise and improve acoustic quality is an important issue in automotive engineering. In this context, this study proposes an improved point connection correction factor by optimizing the division range of the decision circle. The factor corrects the dynamic stiffness of point connections based on wave characteristics, aiming to improve the analysis accuracy of the hybrid FE-SEA model and enhance its ability to model boundary effects. Simulation results show that the proposed method can effectively improve the model’s analysis accuracy, reduce the degrees of freedom in analysis, and increase efficiency, providing important theoretical support and reference for the acoustic package design and NVH performance optimization of new energy vehicles. Full article

Active Speaker Localization (ASL) involves identifying both who is speaking and where they are speaking from within audiovisual content. This capability is crucial in constrained and acoustically challenging environments, such as aircraft cabins during in-flight medical emergencies. In this paper, we propose a novel end-to-end Cross-Modal Audio–Visual Fusion Network (CMAVFN) designed specifically for ASL under real-world aviation conditions, which are characterized by engine noise, dynamic lighting, occlusions from seats or oxygen masks, and frequent speaker turnover. Our model directly processes raw video frames and multi-channel ambient audio, eliminating the need for intermediate face detection pipelines. It anchors spatially resolved visual features with directional audio cues using a cross-modal attention mechanism. To enhance spatiotemporal reasoning, we introduce a dual-branch localization decoder and a cross-modal auxiliary supervision loss. Extensive experiments on public datasets (AVA-ActiveSpeaker, EasyCom) and our domain-specific AirCabin-ASL benchmark demonstrate that CMAVFN achieves robust speaker localization in noisy, occluded, and multi-speaker aviation scenarios. This framework offers a practical foundation for speech-driven interaction systems in aircraft cabins, enabling applications such as real-time crew assistance, voice-based medical documentation, and intelligent in-flight health monitoring. Full article

Passenger comfort in executive-class aircraft demands rigorous control of noise, vibration, and harshness. This study describes the development of a detailed, high-fidelity coupled structural–acoustic finite element model of the Piaggio P.180 passenger cabin, aimed at accurately predicting interior cabin noise within the low- to mid-frequency range. A hybrid discretization strategy was employed to balance computational efficiency and model fidelity. The fuselage structure was discretized using two-dimensional shell elements and one-dimensional beam elements, while the interior cabin air volume was represented using three-dimensional fluid elements. Mesh sizing in both the structural and acoustic domains were determined through analytical wavelength estimates and numerical convergence studies, ensuring appropriate resolution and accuracy. The model’s reliability and accuracy were validated through comprehensive modal analysis. The first three structural modes exhibited strong correlation with available experimental data, confirming the robustness of the numerical model. Subsequent harmonic response analyses were conducted to evaluate the intrinsic noise reduction characteristics of the P.180 airframe, specifically within the frequency range up to approximately 300 Hz. Full article

Nonlinear narrowband low-frequency noise generated during tractors’ operation significantly affects operators’ comfort and working efficiency. Traditional linear active noise control algorithms often struggle to effectively suppress such complex acoustic disturbances. To address this challenge, this paper proposes a momentum-enhanced Volterra filter-based active noise control (ANC) algorithm that improves both the modeling capability of nonlinear acoustic paths and the convergence performance of the system. The proposed approach integrates the nonlinear representation power of the Volterra filter with a momentum optimization mechanism to enhance convergence speed while maintaining robust steady-state accuracy. Simulations are conducted under second- and third-order nonlinear primary paths, followed by performance validation using multi-tone signals and real in-cabin tractor noise recordings. The results demonstrate that the proposed algorithm achieves superior performance, reducing the NMSE to approximately −35 dB and attenuating residual noise energy by 3–5 dB in the 200–800 Hz range, compared to FXLMS and VFXLMS algorithms. The findings highlight the algorithm’s potential for practical implementation in nonlinear and narrowband active noise control scenarios within complex mechanical environments. Full article

Maintaining the required relative humidity values in the vehicle cabin is an important HVAC task, along with considerations related to the temperature, velocity, air pressure and noise. Deviation from the optimal values worsens the psycho-physiological state of the driver and affects the energy efficiency of the train. In this study, a model of liquid film formation on and removal from various cabin surfaces was constructed using the fundamental Navier–Stokes hydrodynamic equations. A special transport model based on the liquid vapor diffusion equation was used to simulate the air environment inside the cabin. The evaporation and condensation of surface films were simulated using the Euler film model, which directly considers liquid–gas and gas–liquid transitions. Numerical results were obtained using the RANS equations and a turbulence model by means of the finite volume method in Ansys CFD. Conjugate fields of temperature, velocity and moisture concentration were constructed for various time intervals, and the dependence values for the film thicknesses on various surfaces relative to time were determined. The verification was conducted in comparison with the experimental data, based on the protocol for measuring the microclimate indicators in workplaces, as applied to the train cabin: the average ranges encompassed temperature changes from 11% to 18%, and relative humidity ranges from 16% to 26%. Comparison with the results of other studies, without considering the phase transition and condensation, shows that, for the warm mode, the average air temperature in the cabin with condensation is 12.5% lower than without condensation, which is related to the process of liquid evaporation from the heated walls. The difference in temperature values for the model with and without condensation ranged from −12.5% to +4.9%. We demonstrate that, with an effective mode of removing condensate film from the window surface, including recirculation modes, the energy consumption of the climate control system improves significantly, but this requires a more accurate consideration of thermodynamic parameters and relative humidity. Thus, considering the moisture condensation model reveals that this variable can significantly affect other parameters of the microclimate in cabins: in particular, the temperature. This means that it should be considered in the numerical modeling, along with the basic heat transfer equations. Full article