Collision enhanced hyper-damping in nonlinear elastic metamaterial

Nonlinear elastic metamaterial, a topic which has attracted extensive attention in recent years, can enable broadband vibration reduction under relatively large amplitude. The combination of damping and strong nonlinearity in metamaterials may entail auxetic effects and offer the capability for low-frequency and broadband vibration reduction. However, there exists a clear lack of proper design methods as well as a deficiency in understanding properties arising from this concept. To tackle this problem, this paper numerically demonstrates that the nonlinear elastic metamaterials, consisting of sandwich damping layers and collision resonators, can generate very robust hyper-damping effect, conducive to efficient and broadband vibration suppression. The collision-enhanced hyper damping is persistently present in a large parameter space, ranging from small to large amplitudes, and for small and large damping coefficient. The achieved robust effects greatly enlarge the application scope of nonlinear metamaterials. We report the design concept, properties and mechanisms of the hyper-damping and its effect on vibration transmission. This paper reveals new properties offered by nonlinear elastic metamaterials, and offers a robust method for achieving efficient low-frequency and broadband vibration suppression.

A Novel Performance Adaptation and Diagnostic Method for Aero-Engines Based on the Aerothermodynamic Inverse Model

Aero-engines are faced with severe challenges of availability and reliability in the increasing operation, and traditional gas path filtering diagnostic methods have limitations restricted by various factors such as strong nonlinearity of the system and lack of critical sensor information. A method based on the aerothermodynamic inverse model (AIM) is proposed to improve the adaptation accuracy and fault diagnostic dynamic estimation response speed in this paper. Thermodynamic mechanisms are utilized to develop AIM, and scaling factors are designed to be calculated iteratively in the presence of measurement correction. In addition, the proposed method is implemented in combination with compensation of the nonlinear filter for real-time estimation of health parameters under the hypothesis of estimated dimensionality reduction. Simulations involved experimental datasets revealed that the maximum average simulated error decreased from 13.73% to 0.46% through adaptation. It was also shown that the dynamic estimated convergence time of the improved diagnostic method reached 2.183 s decrease averagely without divergence compared to the traditional diagnostic method. This paper demonstrates the proposed method has the capacity to generalize aero-engine adaptation approaches and to achieve unbiased estimation with fast convergence in performance diagnostic techniques.

Experimental study on mechanical property and stone-chip resistance of automotive coatings

The damage of automotive coatings caused by stone impact is a problem that has attracted great attention from automotive companies and users. In this work, experiments were conducted to investigate the dynamic tensile properties and stone-chip resistance of automotive coatings. Four kinds of paint films and three typical coatings (single-layer electrocoat coating, single-layer primer coating, and multilayered coating) were used. Under dynamic tensile load using split Hopkinson tension bar (SHTB), the engineering stress-strain curves of the paint films at medium and high strain rates (from 50 to 600 s-1) were obtained. Results indicated that the mechanical properties of the paint films exhibited strong nonlinearity and strain-rate correlation. A modified anti-impact tester was used to complete repeatable single impact tests. The effects of some key parameters, i.e., impact velocity, impact angle, and paint film thickness, on the stone-chip resistance of coatings were systematically investigated. The influence of contact type under high-speed impact conditions was investigated as well. The surface morphologies of the coatings after impact were examined by scanning electron microscopy (SEM), and the failure mechanism of the coatings under normal/oblique impact was discussed. In all experiments, the paint films showed brittle fracture behavior.

Polarization-Independent Large Third-Order-Nonlinearity of Orthogonal Nanoantennas Coupled to an Epsilon-Near-Zero Material

The nonlinear optical response of common materials is limited by bandwidth and energy consumption, which impedes practical application in all-optical signal processing, light detection, harmonic generation, etc. Additionally, the nonlinear performance is typically sensitive to polarization. To circumvent this constraint, we propose that orthogonal nanoantennas coupled to Al-doped zinc oxide (AZO) epsilon-near-zero (ENZ) material show a broadband (~1000 nm bandwidth) large optical nonlinearity simultaneously for two orthogonal polarization states. The absolute maximum value of the nonlinear refractive index n2 is 7.65 cm2∙GW−1, which is 4 orders of magnitude larger than that of the bare AZO film and 7 orders of magnitude larger than that of silica. The coupled structure not only realizes polarization independence and strong nonlinearity, but also allows the sign of the nonlinear response to be flexibly tailored. It provides a promising platform for the realization of ultracompact, low-power, and highly nonlinear all-optical devices on the nanoscale.

Inverse Approach of Parameter Optimization for Nonlinear Meta-Model Using Finite Element Simulation

Accurate and efficient estimation and prediction of the nonlinear behavior of materials during plastic working is a major issue in academic and industrial settings. Studies on property meta-models are being conducted to estimate and predict plastic working results. However, accurately representing strong nonlinear properties using power-law and exponential models, which are typical meta-models, is difficult. The combination meta-model can be used to solve this problem, but the possible number of parameters increases. This causes a cost problem when using FE simulation. In this study, the accuracy of the nonlinear properties of materials and the number of iterations were compared for three typical meta-models and the proposed advanced meta-models considering stress–strain properties. A material property test was conducted using ASTM E8/E8M, and the meta-model was initialized using ASTM E646 and MATLAB Curve Fitting Toolbox. A finite element (FE) simulation was conducted for the meta-models, and the test and simulation results were compared in terms of the engineering stress–strain curve and the root-mean-square error (RMSE). In addition, an inverse method was applied for the FE simulation to estimate the true stress–strain properties, and the results were analyzed in terms of the RMSE and the number of iterations and simulations. Finally, the need for an advanced meta-model that exhibits strong nonlinearity was suggested.

Sensor Fault Diagnosis of Locomotive Electro-Pneumatic Brake Using an Adaptive Unscented Kalman Filter

Normal operation of the pressure sensor is important for the safe operation of the locomotive electro-pneumatic brake system. Sensor fault diagnosis technology facilitates detection of sensor health. However, the strong nonlinearity and variable process noise of the brake system make the sensor fault diagnosis become challenging. In this paper, an adaptive unscented Kalman filter- (UKF-) based fault diagnosis strategy is proposed, aimed at detecting bias faults and drift faults of the equalizing reservoir pressure sensor in the brake system. Firstly, an adaptive UKF based on the Sage-Husa method is applied to accurately estimate the pressure transients in the equalizing reservoir of the brake system. Then, the residual is generated between the estimated pressure by the UKF and the measured pressure by the sensor. Afterwards, the Sequential Probability Ratio Test is used to evaluate the residual so that the incipient and gradual sensor faults can be diagnosed. An experimental prototype platform for diagnosis of the equalizing reservoir pressure control system is constructed to validate the proposed method.

Electrical Properties of GaAs Implanted with High Energy (100meV) 28Si and 120Sn Ions

Single crystal n-GaAs substrates have been implanted at 300 K with 100 MeV 28Si and 120Sn ions to a dose of 1x1018ions/m2 independently. The electrical properties of these samples has been investigated and compared after implantation and annealing up to 850 °C by current voltage (I-V) measurements. It has been observed that the I-V curves for the samples implanted with 28Si ions show p-n junction like characteristics which then show a linear I-V characteristics for the annealing treatment between 150-550 °C. Annealing the samples at 650 °C results in a typical diode like I-V characteristics which become less non-linear after further annealing at 750 °C. Further annealing at 850 °C results in to a back ward diode like behavior. However the I-V curves for the samples implanted with 120Sn ions and annealed up to 450C were linear which then show a weak non linearity for the annealing treatments between 550C-750C. After 850C annealing the samples show a strong nonlinearity typical of a p-n junction. The temperature dependence of resistance of both 28Si and 120Sn implanted GaAs samples after implantation and different annealing steps are investigated and the possible conduction mechanisms are discussed.

Site Amplification during Strong Earthquakes Investigated by Vertical Array Records

A number of vertical array records during eight destructive earthquakes in Japan are utilized, after discussing criteria for desirable requirements of vertical arrays, to formulate seismic amplification between ground surface and outcrop base for seismic zonation. A correlation between peak spectrum amplification and Vs (S-wave velocity) ratio (base Vs/surface Vs) was found to clearly improve by using Vs in an equivalent surface layer wherein predominant frequency or first peak is exerted, though the currently used average Vs in top 30 m is also meaningful, correlating positively with the amplification. We also found that soil nonlinearity during strong earthquakes has only a marginal effect even in soft soil sites on the amplification between surface and outcrop base except for ultimate soil liquefaction failure, while strong nonlinearity clearly appears in the vertical array amplification between surface and downhole base. Its theoretical basis has been explained by a simple study on a two-layered system in terms of radiation damping and strain-dependent equivalent nonlinearity.

Internal Crack Prediction of Continuous Casting Billet Based on Principal Component Analysis and Deep Neural Network

The accurate prediction of internal cracks in steel billets is of great importance for the stable production of continuous casting. However, it is challenging, owing to the strong nonlinearity, and coupling among continuous casting process parameters. In this study, an internal crack prediction model based on the principal component analysis (PCA) and deep neural network (DNN) was proposed by collecting sufficient industrial data. PCA was used to reduce the dimensionality of the factors influencing the internal cracks, and the obtained principal components were used as DNN input variables. The 5-fold cross-validation results demonstrate that the prediction accuracy of the DNN model is 92.2%, which is higher than those of the decision tree (DT), extreme learning machine (ELM), and backpropagation (BP) neural network models. Moreover, the variance analysis showed that the prediction results of the DNN model were more stable. The PCA-DNN model can provide a useful reference for real production, owing to its strong learning ability and fault-tolerant ability.