Multi-body Physics Research Articles

Photoluminescence Enhancement and Carrier Dynamics of Charged Biexciton in Monolayer WS2 Coupled with Plasmonic Nanocavity

Monolayer two-dimensional transition metal dichalcogenide (TMD)-based materials have become one of the ideal platforms for the study of multibody interactions due to their rich excitonic complexes. The coupling between optical nanocavity and material has become an important means for manipulating the optical properties of materials, but there are few studies on the coupling of nanocavities and the multi-body effect in materials. In this study, we investigate the optical properties of silver nanodisk (Ag ND) arrays covering a monolayer WS2. In the experimental sample, we observed a ~114.3-fold photoluminescence enhancement of charged biexciton in the heterostructure region, as compared to the monolayer WS2 region, a value which is much higher than those for exciton (~2.2-fold) and trion (~16.4-fold), a finding which is attributed to the Fano resonant coupling between monolayer WS2 and the Ag ND. By means of time-resolved spectroscopy, we studied the carrier dynamics in the hybrid system. Our findings reveal that resonant coupling promotes the formation and radiation recombination processes of the charged biexciton, significantly reducing the radiative recombination lifetime by ~15-fold, which is much higher than the measurement in exciton (~2-fold). Our results provide an opportunity to understand the multibody physics of coupling with nanocavities, which could facilitate the application of multi-body excitons in the fields of light-emitting devices and lasers, etc.

Interleaved Predictive Control and Planning for an Unmanned Aerial Manipulator With on-the-Fly Rapid Re-Planning in Unknown Environments

Unmanned Aerial Manipulators (UAM) are gaining attention within the unmanned aerial systems research community. They can be used for aerial manipulation tasks such as object retrieval from confined and hard-to-reach spaces. The coupled dynamics between the arm and the base and also the limited flight time would require the development and implementation of optimal motion planning and robust flight control strategies. In this paper, we propose a novel integrated planning and control strategy for object retrieval. A new kinodynamic version of the Rapidly-exploring Random Tree (RRT*), called Lazy-Steering-RRT*, is developed for planning UAM&#x2019;s motion from its start to a pre-grasp state, while keeping the motion of the arm to a minimum. This planning can be carried out on the fly by using Machine-Learning-based techniques to construct the edges in the search tree in a time-efficient way. This facilitates re-planning, as the environment is gradually sensed by limited-range sensors onboard. Once the UAM reaches the pre-grasp state at the end of the motion cycle, an RRT approach is then utilized, where motions of the base and the arm are coordinated for reaching and grasping the object. A novel partitioned control approach, composed of model predictive and PID controllers, is utilized for the UAM to track the planned trajectory. The overall motion planning and control algorithms have been implemented in simulation using Multibody Physics Engines and a number of representative simulation runs are presented. Our results show that the approach is effective in successfully executing the object retrieval task even in confined spaces. <i>Note to Practitioners</i>&#x2014;This research was motivated by the problem of retrieving an object from a confined space such as that in a collapsed building using an unmanned aerial manipulator. Our approach has the following advantages over existing methods: a) Planning and re-planning can be carried out on the fly, b) No prior information about the environment is needed, c) High-frequency energy-optimal control of the UAM is carried out, and d) the planner and the controller are coupled. In this paper, we suggest some novel approaches on both planning and control aspects of the navigation and also on the integration of these two modules to provide a navigation package that meets all the requirements in the retrieval task. Furthermore, our algorithm is computationally-light, therefore, well suited for on the fly planning and control.

Automatic Traction Control for Articulated Off-Road Vehicles

Construction equipment is designed to maintain good traction while operating in difficult off-road conditions. To curb wheel slip, the vehicles are equipped with differential locks. The locks may be engaged/disengaged to switch between two distinct operating modes: the closed mode is characterized by greater off-road passability, while the open mode allows better manueverability. This brief compares three on/off differential lock control algorithms. The algorithms are based on the same kinematic vehicle model, but each relies on the availability of output signals from different sensors. The validity of the kinematic model and the algorithms’ sensitivity to the values assumed by a couple of unobservable states, the wheel slip angles, is investigated by comparison to a realistic articulated hauler model in the multibody physics simulator MSC ADAMS.

Coherent mechanical noise cancellation and cooperativity competition in optomechanical arrays

Studying the interplay between multiple coupled mechanical resonators is a promising new direction in the field of optomechanics. Understanding the dynamics of the interaction can lead to rich new effects, such as enhanced coupling and multi-body physics. In particular, multi-resonator optomechanical systems allow for distinct dynamical effects due to the optical cavity coherently coupling mechanical resonators. Here, we study the mechanical response of two SiN membranes and a single optical mode, and find that the cavity induces a time delay between the local and cavity-transduced thermal noises experienced by the resonators. This results in an optomechanical phase lag that causes destructive interference, cancelling the mechanical thermal noise by up to 20 dB in a controllable fashion and matching our theoretical expectation. Based on the effective coupling between membranes, we further propose, derive, and measure a collective effect, cooperativity competition on mechanical dissipation, whereby the linewidth of one resonator depends on the coupling efficiency (cooperativity) of the other resonator.

Dynamic Simulation of Gear System Based on 2D Space Multibody Physics: A Sustainable Gear Design Approach

The study employed a two-dimensional (2D) space with multibody dynamics as the physics to simulate the dynamic behaviour of intermeshing gears. Both the gear teeth and the gear body were employed to simulate the principal stress and strain as well as the Von Mises stress of the gear system. A pair of meshing teeth were examined from the pinion and the other from the gear for accurate contact stress-strain simulation. The validity of the proposed gear simulation was verified from principal surface stress, Von Mises stress, principal surface strain, elastic strain and total displacement. The results show that the dynamic behaviour of the gear could be attributed to the critical meshing characteristics of the single and double teeth. The peak-to-peak pattern of the Von Mises stress indicates the essential points of stress, which could cause the occurrence of the failure modes. The research of the gear motion study is profoundly enriched and served as a critical reference for gear design.

Attosecond state-resolved carrier motion in quantum materials probed by soft x-ray XANES

Recent developments in attosecond technology led to table-top x-ray spectroscopy in the soft x-ray range, thus uniting the element- and state-specificity of core-level x-ray absorption spectroscopy with the time resolution to follow electronic dynamics in real-time. We describe recent work in attosecond technology and investigations into materials such as Si, SiO2, GaN, Al2O3, Ti, and TiO2, enabled by the convergence of these two capabilities. We showcase the state-of-the-art on isolated attosecond soft x-ray pulses for x-ray absorption near-edge spectroscopy to observe the 3d-state dynamics of the semi-metal TiS2 with attosecond resolution at the Ti L-edge (460 eV). We describe how the element- and state-specificity at the transition metal L-edge of the quantum material allows us to unambiguously identify how and where the optical field influences charge carriers. This precision elucidates that the Ti:3d conduction band states are efficiently photo-doped to a density of 1.9 × 1021 cm−3. The light-field induces coherent motion of intra-band carriers across 38% of the first Brillouin zone. Lastly, we describe the prospects with such unambiguous real-time observation of carrier dynamics in specific bonding or anti-bonding states and speculate that such capability will bring unprecedented opportunities toward an engineered approach for designer materials with pre-defined properties and efficiency. Examples are composites of semiconductors and insulators like Si, Ge, SiO2, GaN, BN, and quantum materials like graphene, transition metal dichalcogens, or high-Tc superconductors like NbN or LaBaCuO. Exiting are prospects to scrutinize canonical questions in multi-body physics, such as whether the electrons or lattice trigger phase transitions.

Deep learning of multibody minimal coordinates

AbstractMultibody systems [1] are the state‐of‐the‐art tool to model complex mechanical mechanisms. However, they typically include redundant coordinates plus constraints, leading to differential algebraic equations for the dynamics which require dedicated integration schemes and control/estimation algorithms.In the deep learning field, autoencoders are a particular neural network class with a symmetric structure to perform a nonlinear dimensionality reduction. In fact, they can be seen as a non‐linear extension of principal component analysis [2].In this work, it is proposed to use an autoencoder to reduce a general multibody model to minimal coordinates, eliminating the constraints. The obtained decoder function is used to perform a non‐linear projection of the multibody coordinates: urn:x-wiley:16177061:media:PAMM202000348:pamm202000348-math-0001 where are respectively the encoder and decoder neural network functions, while are respectively the obtained minimal coordinates and reconstructed full multibody coordinates.The novelty is that the autoencoder training includes the multibody physics information [3]. In this way, the autoencoder does not only perform a dimensionality reduction of the original coordinates but can be used for model order reduction. The obtained reduced‐order model consists of ordinary differential equations (with respect to the original differential algebraic equations) and can be used to perform dynamic simulations and easily coupled with standard estimation algorithms for state‐estimation.A typical application example is a mechanism with closed‐loops, such as a car suspension, for which no analytical expression is available to reduce the full multibody model to minimal coordinates. The introduced physics‐informed neural network allows to reduce the model and combine it with an augmented extended Kalman filtering scheme [4], in order to estimate difficult‐to‐measure inputs or parameters in the system.In a first application, the model in combination with measured strut displacement or acceleration, allows to estimate the strut force, in addition to the body full coordinates in position, velocity and acceleration. The rationale is reported in the figure below.rationale.pngThe Flanders Innovation &amp; Entrepreneurship Agency within the IMPROVED project and the AI impulse program of the Flemish Government are gratefully acknowledged for their support.

Learning Damage Event Discriminator Functions with Distributed Multi-instance RNN/LSTM Machine Learning - Mastering the Challenge

Common Structural Health Monitoring systems are used to detect past damages occurred in structures with sensor networks and external sensor data processing. The time of the damage creation event is commonly unknown. Numerical methods and Machine Learning are used to extract relevant damage information from sensor signals that is characterised by a high data volume and dimension. In this work, distributed multi-instance learning applied to raw time-series of sensor data is deployed to predict the event of the occurrence of a hidden damage in a mechanical structure using typical vibrations of the structure. The sensor processing and learning is performed locally on sensor node level with a global fusion of prediction results to estimate the damage location and the time of the damage creation. Recurrent neural networks with a long-short-term memory architecture are considered implementing a damage discriminator function. The sensor data required for the evaluation of the proposed approach is generated by a multi-body physics simulation approximating material properties.

4D Printing of Magnetically Functionalized Chainmail for Exoskeletal Biomedical Applications

AbstractChainmail fabrics manufactured by selective laser sintering 3D printing have been magnetically functionalized to create a lightweight, 4D printed, actuating fabric. The post-processing method involves submerging the porous prints in commercial ferrofluid (oil-based magnetic liquid), followed by drying under heat. The actuation of the chainmail has been simulated using a rigid multi-body physics engine, and qualitatively matches experiment. Such magnetically actuating fabrics have potential to make thin, lightweight and comfortable wearable assistive devices.

The benefits of mechatronically-guided railway vehicles: A multi-body physics simulation study

Mechatronically-guided railway vehicles are of paramount importance in addressing the increasing interest in reducing wheel-rail wear and improving guidance and steering. Conventional passively-guided rail vehicles are limited by the mechanical constraints of the suspension elements. Currently, a typical rail vehicle suspension needs to be sufficiently stiff to stabilize the wheelsets while being complaint enough to negotiate curved track profiles. The suspension is therefore a compromise for the contradictory requirements of curving and stability.In mechatronic vehicles, actuators are used with the conventional suspension components to provide additional stiffness or damping forces needed to optimise a vehicle for a wide variety of scenarios, and not rely on a sub optimal combination of passive components.This research demonstrates the benefits of active guidance and steering when compared to a conventional vehicle using simulation results from a multi-body simulation software Simpack. It also provides insights into the relative performance of the mechatronic schemes. The Simpack modeling allows for a complex model with high fidelity, which provides an additional level of proof of the control algorithms working on a real rail vehicle. Each vehicle is assessed in terms of guidance on straight track, steering on curved track, actuation requirements and wheel-rail wear. Significant benefits are demonstrated in one of the guided vehicles with independently-rotating wheelsets.

Contact force estimation in the wheel/rail interface for curving scenarios through regions of reduced adhesion

Regions of extreme low-adhesion between the wheel and rail can cause critical problems in traction and braking. This can manifest in operational issues such as signals being passed at danger, or pessimistic network wide responses to mitigate for localised issues. Poor traction conditions can be caused by oil contaminants, rain, ice, condensation of water droplets (micro-wetting) or leaves on the line, where compressed leaf contamination can cause a rapid decrease in adhesion. The complexity of the problem arises as a result of the inability to directly measure and monitor all the factors involved. There remains a lack of real-time information regarding the state and location of low-adhesion areas across rail networks. On-board low adhesion detection technology installed to in-service vehicles is a suggested method to capture up-to-date adhesion information network wide and minimise significant disruptions and cancellations in railway schedules. This paper extends a principle of a model-based estimation technique previously developed in straight track running for operating in a curving scenario. The vehicle model of focus here will be a high-fidelity, multi-body physics representation of a full-vehicle.

Validation of a squeeze-film-damper test rig by using multibody cosimulation

Squeeze-film dampers (SFD) are applied to provide damping at the outer ring of ball bearings used in jet engines. The main functions are to cap rotor oscillations and to reduce dynamic forces. A new test rig was designed to allow an orbital motion of the rotor shaft excited by two independently controllable electromagnetic shakers. In order to achieve this excitation, the two shakers are rigidly coupled together; due to this, a degree of freedom is missing in this mechanism. The precisely adjusted stiffness of the coupling elements acts as a solid joint to provide the missing DOF. The resulting shear forces acting on the opposite shaker are supported by a support structure, designed as part of the coupling elements. A multibody cosimulation and several other virtual studies are performed to verify and validate the dynamic behavior of the test rig. The SFD behavior cannot be implemented in the cosimulation by using a standard library damping element of the simulation tools because its behavior is nonlinear due to its design and functionality. It is implemented according to its valid theory, based on Reynolds equations for plain bearings. The calculation of the multibody physics takes place in MSC Adams, whereas the theory of the SFD is described and implemented as a mathematical model in MATLAB/Simu link. A both-ends-against-the-middle approach is realized and discussed.