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.

Voros Coefficients at the Origin and at the Infinity of the Generalized Hypergeometric Differential Equations with a Large Parameter

Voros coefficients of the generalized hypergeometric differential equations with a large parameter are defined and their explicit forms are given for the origin and for the infinity. It is shown that they are Borel summable in some specified regions in the space of parameters and their Borel sums in the regions are given.

Shattered Pellet Injection experiments at JET in support of the ITER Disruption Mitigation System design

A series of experiments have been executed at JET to assess the efficacy of the newly installed Shattered Pellet Injection (SPI) system in mitigating the effects of disruptions. Issues, important for the ITER disruption mitigation system, such as thermal load mitigation, avoidance of runaway electron formation, radiation asymmetries during thermal quench mitigation, electromagnetic load control and runaway electron energy dissipation have been addressed over a large parameter range. The efficiency of the mitigation has been examined for the various SPI injection strategies. The paper summarises the results from these JET SPI experiments and discusses their implications for the ITER disruption mitigation scheme.

Memristive Computation-Oriented Chaos and Dynamics Control

A variable boostable chaotic system and the Hindmarsh–Rose neuron model are applied for observing the dynamics revised by memristive computation. Nonlinearity hidden in a memristor makes a dynamic system prone to be chaos. Inherent dynamics in a dynamic system can be preserved in specific circumstances. Specifically, as an example, offset boosting in the original system is inherited in the derived memristive system, where the average value of the system variable is rescaled linearly by the offset booster. Additional feedback from memristive computation raises chaos, as a case, in the Hindmarsh–Rose neuron model the spiking behavior of membrane potential exhibits chaos with a relatively large parameter region of the memristor.

Germanium Quantum-Dot Array with Self-Aligned Electrodes for Quantum Electronic Devices

Semiconductor-based quantum registers require scalable quantum-dots (QDs) to be accurately located in close proximity to and independently addressable by external electrodes. Si-based QD qubits have been realized in various lithographically-defined Si/SiGe heterostructures and validated only for milli-Kelvin temperature operation. QD qubits have recently been explored in germanium (Ge) materials systems that are envisaged to operate at higher temperatures, relax lithographic-fabrication requirements, and scale up to large quantum systems. We report the unique scalability and tunability of Ge spherical-shaped QDs that are controllably located, closely coupled between each another, and self-aligned with control electrodes, using a coordinated combination of lithographic patterning and self-assembled growth. The core experimental design is based on the thermal oxidation of poly-SiGe spacer islands located at each sidewall corner or included-angle location of Si3N4/Si-ridges with specially designed fanout structures. Multiple Ge QDs with good tunability in QD sizes and self-aligned electrodes were controllably achieved. Spherical-shaped Ge QDs are closely coupled to each other via coupling barriers of Si3N4 spacer layers/c-Si that are electrically tunable via self-aligned poly-Si or polycide electrodes. Our ability to place size-tunable spherical Ge QDs at any desired location, therefore, offers a large parameter space within which to design novel quantum electronic devices.

Film thickness in elastohydrodynamically lubricated slender elliptic contacts: Part I – numerical studies of central film thickness

In this paper, analytical equations for the central film thickness in slender elliptic contacts are investigated. A comparison of state-of-the-art formulas with simulation results of a multilevel elastohydrodynamic lubrication solver is conducted and shows considerable deviation. Therefore, a new film thickness formula for slender elliptic contacts with variable ellipticity is derived. It incorporates asymptotic solutions, which results in validity over a large parameter domain. It captures the behaviour of increasing film thickness with increasing load for specific very slender contacts. The new formula proves to be significantly more accurate than current equations. Experimental studies and discussions on minimum film thickness will be presented in a subsequent publication.

A new pendulum motion with a suspended point near infinity

In this paper, a pendulum model is represented by a mechanical system that consists of a simple pendulum suspended on a spring, which is permitted oscillations in a plane. The point of suspension moves in a circular path of the radius (a) which is sufficiently large. There are two degrees of freedom for describing the motion named; the angular displacement of the pendulum and the extension of the spring. The equations of motion in terms of the generalized coordinates $$\varphi$$ φ and $$\xi$$ ξ are obtained using Lagrange’s equation. The approximated solutions of these equations are achieved up to the third order of approximation in terms of a large parameter $$\varepsilon$$ ε will be defined instead of a small one in previous studies. The influences of parameters of the system on the motion are obtained using a computerized program. The computerized studies obtained show the accuracy of the used methods through graphical representations.

Deep reinforcement learning for efficient measurement of quantum devices

Deep reinforcement learning is an emerging machine-learning approach that can teach a computer to learn from their actions and rewards similar to the way humans learn from experience. It offers many advantages in automating decision processes to navigate large parameter spaces. This paper proposes an approach to the efficient measurement of quantum devices based on deep reinforcement learning. We focus on double quantum dot devices, demonstrating the fully automatic identification of specific transport features called bias triangles. Measurements targeting these features are difficult to automate, since bias triangles are found in otherwise featureless regions of the parameter space. Our algorithm identifies bias triangles in a mean time of <30 min, and sometimes as little as 1 min. This approach, based on dueling deep Q-networks, can be adapted to a broad range of devices and target transport features. This is a crucial demonstration of the utility of deep reinforcement learning for decision making in the measurement and operation of quantum devices.

Profit Seeking versus Survival Seeking: Green Investment of Capital-Constrained Suppliers with Incentive Contracts

This study explores a supply chain with a capital-constrained startup supplier who invests in product greenness and a manufacturer who sells green products to consumers under demand uncertainty. Green investment of the supplier is supported by the manufacturer with two incentive contracts: (i) investment- and (ii) revenue-sharing contracts. Profit- and survival-seeking objectives are considered for the startup supplier. Results show that the profit-seeking supplier increases its product greenness if demand uncertainty rises, whereas the survival-seeking supplier increases its product greenness if its capital constraints increase. Compared with the commonly used wholesale price contract, investment- and revenue-sharing contracts can help facilitate the “win-win” supply chain cooperation for improving product greenness. If the profit-seeking supplier cooperates with the manufacturer, the investment-sharing contract is preferred as the demand uncertainty increases. If a survival-seeking supplier cooperates with the manufacturer, the revenue-sharing contract is preferred as the capital constraint increases. Overall, the revenue-sharing contract is preferred given the high attractiveness of the green investment. By extending the discussion into two periods, the revenue-sharing contract will be preferred in the survival-seeking case because the cooperation can continue in a large parameter space.