Crashworthiness design of density-graded cellular metals

AbstractCrashworthiness of cellular metals with a linear density gradient was analyzed by using cell-based finite element models and shock models. Mechanisms of energy absorption and deformation of graded cellular metals were explored by shock wave propagation analysis. Results show that a positive density gradient is a good choice for protecting the impacting object because it can meet the crashworthiness requirements of high energy absorption, stable impact resistance and low peak stress

Filter Design and Consistency Evaluation for 3D Tongue Motion Estimation using Harmonic Phase Analysis Method

Understanding patterns of tongue motion in speech using 3D motion estimation is challenging. Harmonic phase analysis has been used to perform noninvasive tongue motion and strain estimation using tagged magnetic resonance imaging (MRI). Two main contributions have been made in this thesis. First, the filtering process, which is used to produce harmonic phase images used for tissue tracking, influences the estimation accuracy. For this work, we evaluated different filtering approaches, and propose a novel high-pass filter for volumes tagged in individual directions. Testing was done using an open benchmarking dataset and synthetic images obtained using a mechanical model. Second, the datasets with inconsistent motion need to be excluded to yield meaningful motion estimation. For this work, we used a tracking-based method to evaluate the motion consistency between datasets and gave a strategy to identify the inconsistent dataset. Experiments including 2 normal subjects were done to validate our method. In all, the first work about 3D filter design improves the motion estimation accuracy and the second work about motion consistency test ensures the meaningfulness of the estimation results

Iterative learning control for multi-agent systems with impulsive consensus tracking

In this paper, we adopt D-type and PD-type learning laws with the initial state of iteration to achieve uniform tracking problem of multi-agent systems subjected to impulsive input. For the multi-agent system with impulse, we show that all agents are driven to achieve a given asymptotical consensus as the iteration number increases via the proposed learning laws if the virtual leader has a path to any follower agent. Finally, an example is illustrated to verify the effectiveness by tracking a continuous or piecewise continuous desired trajectory

Multi-objective optimization design for a battery pack of electric vehicle with surrogate models

In this investigation, a systematic surrogate-based optimization design framework for a battery pack is presented. An air-cooling battery pack equipped on electric vehicles is first designed. Finite element analysis (FEA) results of the baseline design show that global maximum stresses under x-axis and y-axis transient acceleration shock condition are both above the tensile limit of material. Selecting the panel and beam thickness of battery pack as design variables, with global maximum stress constraints in shock cases, a multi-objective optimization problem is implemented using metamodel technique and multi-objective particle-swarm-optimization (MOPSO) algorithm to simultaneously minimize the total mass and maximize the restrained basic frequency. It is found that 2nd order polynomial response surface (PRS), 3rd order PRS and radial basis function (RBF) are the most accurate and appropriate metamodels for restrained basic frequency, global maximum stresses under x-axis and y-axis shock conditions respectively. Results demonstrate that all the optimal solutions in Pareto Frontier have heavier weight and lower frequency compared with baseline design due to the restriction of global maximum stress response. Finally, two optimal schemes, “Knee Point” and “lightest weight”, satisfied both of the stress constraint conditions, show great consistency with FEA results and can be selected as alternative improved schemes

DYNAMIC ANALYSIS OF THE RIGID-FLEXIBLE EXCAVATOR MECHANISM BASED ON VIRTUAL PROTOTYPE

In this paper, the excavator’s dynamic performance is considered together with the study of its trajectory, stress distribution and vibration. Many researchers have focused their study on the kinematics principle while a few others focused their work on dynamic performance, especially the vibration analysis. Previous studies of dynamic performance analysis have ignored the vibration effects. To address these challenges, the rigid-flexible coupling model of the excavator attachment is established and carried out based on virtual prototyping in this study. The dipper handle, the boom and the hoist rope are modeled as a flexible multi-body system for structural strength. The other components are modeled as a rigid multi-body system to catch the dynamic characteristics. The results show that the number of flexible bodies has little effect on the excavation trajectory. The maximum stress determined for the dipper handle and the boom are 96.45 MPa and 212.24 MPa, respectively. The dynamic performance of the excavator is greatly influenced by the clearance and is characterized by two phases: as the clearance decreases, the dynamic response decreases at first and then increases

Iterative learning control for impulsive multi-agent systems with varying trial lengths

In this paper, we introduce iterative learning control (ILC) schemes with varying trial lengths (VTL) to control impulsive multi-agent systems (I-MAS). We use domain alignment operator to characterize each tracking error to ensure that the error can completely update the control function during each iteration. Then we analyze the system’s uniform convergence to the target leader. Further, we use two local average operators to optimize the control function such that it can make full use of the iteration error. Finally, numerical examples are provided to verify the theoretical results

A duplication-free quantum neural network for universal approximation

The universality of a quantum neural network refers to its ability to approximate arbitrary functions and is a theoretical guarantee for its effectiveness. A non-universal neural network could fail in completing the machine learning task. One proposal for universality is to encode the quantum data into identical copies of a tensor product, but this will substantially increase the system size and the circuit complexity. To address this problem, we propose a simple design of a duplication-free quantum neural network whose universality can be rigorously proved. Compared with other established proposals, our model requires significantly fewer qubits and a shallower circuit, substantially lowering the resource overhead for implementation. It is also more robust against noise and easier to implement on a near-term device. Simulations show that our model can solve a broad range of classical and quantum learning problems, demonstrating its broad application potential.Comment: 15 pages, 10 figure

Suppression of laser beam's polarization and intensity fluctuation via a Mach-Zehnder interferometer with proper feedback

Long ground-Rydberg coherence lifetime is interesting for implementing high-fidelity quantum logic gates, many-body physics, and other quantum information protocols. However, the potential well formed by a conventional far-off-resonance red-detuned optical-dipole trap that is attractive for ground-state cold atoms is usually repulsive for Rydberg atoms, which will result in the rapid loss of atoms and low repetition rate of the experimental sequence. Moreover, the coherence time will be sharply shortened due to the residual thermal motion of cold atoms. These issues can be addressed by a one-dimensional magic lattice trap, which can form a deeper potential trap than the traveling wave optical dipole trap when the output power is limited. In addition, these common techniques for atomic confinement generally have certain requirements for the polarization and intensity stability of the laser. Here, we demonstrated a method to suppress both the polarization drift and power fluctuation only based on the phase management of the Mach-Zehnder interferometer for a one-dimensional magic lattice trap. With the combination of three wave plates and the interferometer, we used the instrument to collect data in the time domain, analyzed the fluctuation of laser intensity, and calculated the noise power spectral density. We found that the total intensity fluctuation comprising laser power fluctuation and polarization drift was significantly suppressed, and the noise power spectral density after closed-loop locking with a typical bandwidth of 1-3000 Hz was significantly lower than that under the free running of the laser system. Typically, at 1000 Hz, the noise power spectral density after locking was about 10 dB lower than that under the free running of a master oscillator power amplifier system.The intensity-polarization control technique provides potential applications