Mode I Delamination Fracture Characterization of Polymeric Composites under Elevated Temperature

Delamination is one of the major failure modes seen in the laminated polymeric matrix composite (PMC). Accurate prediction of delamination initiation and propagation is important for the design and analysis of robust composite structures. Existing experimental methodologies that are based on linear elastic fracture mechanics are inadequate to characterize delamination fracture properties under elevated temperature when PMC properties become time-, loading-history, and rate-dependent. A new experimental methodology based on linear viscoelastic fracture theory is developed and verified through finite element analysis and experiments. This new technique determines crack growth curves, such as stress intensity factor vs. crack growth speed and fracture initiation energy vs. crack initiation time, through the experimentally determined J-integral, Js, for a linear viscoelastic double cantilever beam (DCB) specimen. Special test setup is designed and validated for determining accurate Js using just the applied load and the load end rotation angles. This new methodology is then applied to measure the mode I fracture properties of a highly toughened graphite/epoxy composite under various environmental conditions

Optimizing Gear Shifting Strategy for Off-Road Vehicle with Dynamic Programming

Gear shifting strategy of vehicle is important aid for the acquisition of dynamic performance and high economy. A dynamic programming (DP) algorithm is used to optimize the gear shifting schedule for off-road vehicle by using an objective function that weighs fuel use and trip time. The optimization is accomplished through discrete dynamic programming and a trade-off between trip time and fuel consumption is analyzed. By using concave and convex surface road as road profile, an optimal gear shifting strategy is used to control the longitudinal behavior of the vehicle. Simulation results show that the trip time can be reduced by powerful gear shifting strategy and fuel consumption can achieve high economy with economical gear shifting strategy in different initial conditions and route cases

Feedback Linearization Control for Path Tracking of Articulated Dump Truck

The articulated dump truck is a widespread tansport vehicle for narrow rough terrain environment. To achieve the autonomous driving in the underground tunnel, this ariticle proporses a path following strategy of articulated vehicle based on feedback linearization algorithm. Fisrt of all, the articulated vehicle kinematics model, which reflects the relationship of the structure parameters and state variables, is established. Refering to the model, the nonlinear errors equation between real path and reference path, which are as the feedback from the path tracking process, is solved and linearized. After estimating the system controllable, according to the error equation, the path following controller with feedback linearization algorithm is designed through calaculating the parameters with the pole assignment. Finally, the hardware in the loop simulation on NI cRIO and PXI controller is lunched for verifying the control quality and real-time path tracking performance

Characteristics Analysis of Non-linear Torsional Vibration in Engine and Generator Shafting system

To solve the non-linear torsional vibration problem of engine and generator shafting causing body structural vibration and noise in motorized wheel vehicle, where the engine and the generator connected directly. First of all, analysis the characteristics of the shafting system, besides the external shock excitation of engine and generator. Then, through lumped parameter model method, mathematical model of the non-linear torsional vibration was established, which could reflect the dynamic characteristics of the system. Analysis the effect of mechanical parameters and electromagnetic parameters on the shafting. And get the non-linear differential equations of the system torsional vibration, which expresses the relation between structural parameters, electromagnetic parameters and the system dynamic characteristics. And multiple scales method was used to solve the equations. Non-contact measurement method was used in the torsional vibration test. Finally, consistency of the results, indicate that the research method used is reliability and accuracy, and get the critical speed of the shafting torsional vibration

Layer-Adapted Implicit Distribution Alignment Networks for Cross-Corpus Speech Emotion Recognition

In this paper, we propose a new unsupervised domain adaptation (DA) method called layer-adapted implicit distribution alignment networks (LIDAN) to address the challenge of cross-corpus speech emotion recognition (SER). LIDAN extends our previous ICASSP work, deep implicit distribution alignment networks (DIDAN), whose key contribution lies in the introduction of a novel regularization term called implicit distribution alignment (IDA). This term allows DIDAN trained on source (training) speech samples to remain applicable to predicting emotion labels for target (testing) speech samples, regardless of corpus variance in cross-corpus SER. To further enhance this method, we extend IDA to layer-adapted IDA (LIDA), resulting in LIDAN. This layer-adpated extention consists of three modified IDA terms that consider emotion labels at different levels of granularity. These terms are strategically arranged within different fully connected layers in LIDAN, aligning with the increasing emotion-discriminative abilities with respect to the layer depth. This arrangement enables LIDAN to more effectively learn emotion-discriminative and corpus-invariant features for SER across various corpora compared to DIDAN. It is also worthy to mention that unlike most existing methods that rely on estimating statistical moments to describe pre-assumed explicit distributions, both IDA and LIDA take a different approach. They utilize an idea of target sample reconstruction to directly bridge the feature distribution gap without making assumptions about their distribution type. As a result, DIDAN and LIDAN can be viewed as implicit cross-corpus SER methods. To evaluate LIDAN, we conducted extensive cross-corpus SER experiments on EmoDB, eNTERFACE, and CASIA corpora. The experimental results demonstrate that LIDAN surpasses recent state-of-the-art explicit unsupervised DA methods in tackling cross-corpus SER tasks