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• The bidirectional knowledge graph of the tire/road airborne sound quality is proposed. • A multitarget prediction method based on the adaptive balanced learning mechanism for ResNet is proposed. • A knowledge- and data-driven method is developed for the prediction and optimization of TRA sound quality. • The proposed method overperformed the conventional prediction and optimization methods in effectiveness and robustness. With the promotion of pure electric vehicles (PEVs), the overall interior noise level has been gradually reduced. Tire/road noise is increasingly becoming noticeable in PEVs and represents a primary concern for passengers. Vehicle acoustic packages are crucial for suppressing tire/road noise, and numerous studies have focused on improving the acoustic package performance and sound quality of tire/road noise. However, the prediction and optimization of tire/road acoustic comfort have two deficiencies: (1) The characteristics and transfer paths of tire/road noise are complex. Using knowledge-driven methods (such as simulation models) and data-driven methods (such as neural networks) for analysis has difficulty in accuracy parameter acquisition and unexplainable models. (2) The sound quality of tire/road noise is multidimensional. In the development of multitarget prediction models, when multiple targets contradict each other, prediction bias may result from wide differences among targets in the gradient value of the loss function in training. Therefore, in this paper, a knowledge- and data-driven method is proposed, which introduces the knowledge graph technique to develop a vehicle tire/road noise knowledge graph architecture and uses an improved residual network to drive reasoning in the domain knowledge graph. In addition, a multitarget prediction method based on the adaptive balanced learning mechanism for a residual network is proposed, which uses the dynamic weighted average method to adaptively adjust the loss weight of each target according to the convergence speed and learning difficulty of each target. The proposed dual-drive method is applied to predict and optimize the performance of vehicle acoustic package and to further improve the multiple sound quality metrics of tire/road noise. In the experimental validation, the proposed method outperforms the traditional prediction and optimization methods in effectiveness and robustness. [ABSTRACT FROM AUTHOR]

• A knowledge- and data-driven approach is proposed for vehicle acoustic package system. • The proposed ALRF-LSTM can decrease and increase the learning rate adaptively. • The proposed MLMO structure can reduce the complexity of the problem. • The acoustic package is optimized through the knowledge-and data-driven method. The sound absorption and sound insulation performance of an acoustic package (AP) system directly affect the noise, vibration and harshness performance of a vehicle. Numerous studies have studied the optimization of vehicle sound package, however, there are two deficiencies in the current research of sound package: (1) The noise transmission path of acoustic package is complex and hierarchical. Most of the related works focus on the data-driven part while ignoring the knowledge attributes behind the acoustic package design problem, which limits the further improvement of prediction and optimization of acoustic package performance. (2) In using intelligent neural networks-based methods such as long short-term memory (LSTM), reducing the learning rate during training gradually narrows the search interval of a solution, and adjusting the learning rate in a small range may tend to trap local optima. In this study, a knowledge- and data-driven approach is proposed for the development of acoustic package systems. A multiple-level multiple-object method is proposed as the knowledge model, and a multilayer structure of the acoustic package system that contains the system, subsystem and component layers is developed. In addition, an improved long short-term memory model based on an adaptive learning rate forest, which can increase and decrease the learning rate adaptively, is proposed as the data-driven model. The knowledge- and data-driven method is applied to optimize the sound absorption and insulation of the acoustic package system. In the experimental validation, the effectiveness and robustness of the proposed method outperformed the traditional direct mapping method and the conventional long short-term memory method. [ABSTRACT FROM AUTHOR]

[Display omitted] • An improved interval analysis method (IIAM) for uncertain optimization is proposed. • A group paired comparison method (GPCM) is proposed. • The tire/road structure-borne (TRS) noise of PEVs is objectively and subjectively evaluated. • Contribution factors that influence the TRS noise of PEVs are analyzed. • The TRS noise of PEVs is optimized and compared through the IIAM and GA. Without the masking effect of engine noise, tire/road (TR) noise is increasingly becoming noticeable in pure electric vehicles (PEVs) and represents a primary concern for drivers and passengers. Currently, numerous works have studied PEV motor and powertrain noises, but few studies have investigated the tire/road structure-borne (TRS) noise of PEVs. Therefore, in this paper, the sound quality of TRS noise is studied through objective and subjective evaluations, and the group paired comparison method (GPCM) is proposed to evaluate a large noise sample set. The correlation between sound quality metrics and the subjective annoyance of TRS noise is analyzed, and the contribution of chassis dynamic parameters to the TRS noise of PEVs is quantified. In addition, because of the nonlinear transfer and complex characteristics of TRS noise, the expected design results will easily be affected by material, processing and manufacturing uncertainties, which are difficult to process with conventional optimization methods. Therefore, to overcome the uncertainty problem, an improved interval analysis method (IIAM) is proposed. This method is used to optimize the interior TR sound quality of PEVs while treating riding comfort as a constraint. The optimized result of the IIAM is compared with that of the advanced genetic algorithm (GA) optimization method. Through real vehicle verification, the proposed IIAM outperforms the GA method in terms of accuracy and robustness. [ABSTRACT FROM AUTHOR]

Abstract: In many lattice Boltzmann studies about bubble rising, mass conservation is not satisfactory and the terminal bubble rising shape or velocity is not so consistent with experimental data as those obtained through other CFD techniques. In this paper, based on the multiphase model (He et al., 1999 [1]), a mass-conserving axisymmetric multiphase lattice Boltzmann model is developed. In the model, a mass correction step and an effective surface tension formula are introduced into the model. We demonstrate how the macroscopic axisymmetric Cahn–Hilliard equation and Navier–Stokes equation are recovered from the lattice Boltzmann equations through Chapman–Enskog expansion. The developed model is applied to simulate the bubble rising in viscous fluid. The mass correction step in our scheme significantly improves the bubble mass conservation. The surface tension calculation successfully predicts the terminal bubble shapes and reproduces the effect of initial bubble shape. The terminal bubble rising velocities are very consistent with experimental and numerical data in the literature. Qualitatively, the wakes behind the bubbles also agree well with experimental data. This model is useful for predicting the axisymmetric two-phase flows. [Copyright &y& Elsevier]

• Deep CNNs with an adaptable learning rate tree (ALRT-CNNs) is proposed. • ALRT-CNNs can adaptively reduce and increase the learning rate. • The initial learning rate has a small effect in ALRT-CNNs. • The nonstationary interior noises are objectively and subjectively evaluated. • Contribution of sound quality to the nonstationary interior noise is analyzed. Vehicle nonstationary interior noise has nonstationary characteristics that negatively affect the sound annoyance of passengers. Currently, there are some deficiencies in the research of vehicle interior nonstationary noise. (1) Numerous works have studied conventional vehicle interior noise, but limited works have investigated PEV interior noise. (2) Few studies have examined the sound characteristics of vehicle nonstationary interior noise (acceleration and braking conditions). (3) In using intelligent prediction methods such as deep convolutional neural networks (CNNs), reducing the learning rate during training gradually narrows the search range of a solution and becomes trapped in local optima. Consequently, the nonstationary interior noise of PEVs is studied in this paper. A method for quantitative sound quality prediction of the PEV nonstationary interior noise based on tacho-tracking psychoacoustic metrics and deep CNNs with adaptable learning rate trees (ALRT-CNNs) is presented to solve the aforementioned problems. There are two original contributions of this paper. First, ALRT-CNNs can adaptively reduce and increase the learning rate based on the training loss, and an appropriate search range for a better solution can be obtained. Second, the proposed prediction method can comprehensively reflect the nonstationary sound characteristics of PEV nonstationary interior noise as well as their influence on human subjective annoyance. [ABSTRACT FROM AUTHOR]

Tire vibration includes not only radial vibration, but also lateral vibration. The lateral vibration always plays an important role to vehicle Noise, Vibration and Harshness (NVH), especially high-order lateral bending vibration. In this paper, a new theoretical three-dimensional ring based model and related new displacement function were both developed for tire radial and lateral vibrations, especially for tire high-order bending vibration. The proposed model considers the detailed features of the 3D ring such as the ring thickness, the shear strain in lateral-tangential plane and lateral normal strain, tangential and lateral pre-stress induced by inflation. The new displacement function in Dual Flourier series was developed for the tire model, which can in particular describe the belt bending vibrations in transverse direction. Either of the proposed model and displacement function is indispensable to compute high-order modes of the belt. The proposed model was verified with the conducted physical experiments. The proposed model costs less than 5 s to compute the natural frequencies of tire. The results show that the tire vibration modes from 300 Hz to 500 Hz could be predicted quite well as well as that below 300 Hz. The model is an efficient and effective tool to investigate the dynamic characteristics of tire. [ABSTRACT FROM AUTHOR]

Spleen is one of the crucial sites for cellular and humoral immunity but it easily damaged during pathogenic infections resulting in immunosuppression. The current study was therefore performed to explore the mechanism of acute spleen injury induced by salmonella lipopolysaccharide (LPS) in young chicks. Healthy one-day-old Cobb strain broiler chicks were intra-peritoneally injected with saline or LPS. LPS treatment caused significant decreases in body and spleen weights at 36 and 72 h. Histological analysis showed the changes of ellipsoid structures with beginning of nuclear pyknosis and karyolysis similar to steatosis at 12 h, maximum histopathological lesions were seen at 36 h, however these were disappeared at 72 h post LPS stimulation. Cell proliferation was decreased (low PCNA positivity) and apoptosis increased (high ssDNA positivity) in the spleen at 12 and 36 h after LPS treatment. The expression levels of mRNA for caspase-3, caspase-8, B-cell lymphoma 2 (BCL-2), tumor protein p53 or p53 and Bcl-2 homologous antagonist killer (BAK) showed slight increase at some time points following LPS stimulation. LPS treatment also induced significant up-regulation in toll like receptor 4 (TLR4) at 36 h post LPS stimulation and slight increase in expressions of its downstream molecules (MyD88 and NF-κB) at 12 h post LPS treatment. The keystone cytokines (TNF-α and IL-6) exhibited significant up-regulation at 12 h following LPS stimulation. Our findings provided novel information about the histopathological as well as apoptotic and proliferative alterations in spleen mediated by TLR4 signaling induced by Salmonella LPS in avian species. [ABSTRACT FROM AUTHOR]

Grape pomace (GP) management has been a challenge worldwide. We have previously demonstrated a biorefinery process to recover oil and polyphenols, and produce biofuels from GP sequentially, although over 50% of GP solid waste remains post-processing. To approach zero solid waste during GP processing, herein a pyrolysis process was designed for converting GP and its secondary processing wastes to biochars, which were then evaluated for lead (Pb) adsorption from water. GP lignin pyrolyzed at 700 °C (GPL2 700 biochar) with specific surface area of 485 m2/g showed the highest Pb adsorption capacity, and achieved 66.5% of Pb removal from an initially high concentration of 300 mg/L within 30 min. At low initial Pb concentrations (50–3000 μ g/L), GPL2 700 biochar could reduce Pb concentrations to 0.208–77.2 μ g / L. In addition, experimental and modeling results revealed that both physisorption and chemisorption mechanisms were involved in the adsorption process of GPL2 700 biochar. • Grape pomace (GP) and its secondary waste were used for biochar production. • GP lignin derived biochar (GPL2 700) showed the highest Pb adsorption capacity. • GPL2 700 showed good adsorption at a low Pb level in contaminated drinking water. • Chemisorption and physisorption mechanisms accounted for Pb removal of GPL2 700. [ABSTRACT FROM AUTHOR]