Direct Dynamics Study on CH_2O + CH_3~·→ CHO + CH_4 Reaction

It is still a formidable challenge to study CH2O ＋ CH3·→ CHO ＋ CH4 reaction in the gas phase by traditional dynamics, because of the large number of freedom degrees for the system. In this paper, direct dynamics, in which trajectories were run directly on the DFT potential energy surface, have been applied to the reaction, which gave a direct look in the reaction processes. Two sets of trajectories at different initial orientations of reactants and temperature have been simulated. And the detailed reaction mechanisms have been described.

All-Atom Direct Folding Simulation for Proteins Using the Accelerated Molecular Dynamics in Implicit Solvent Model

We report the results of protein folding （219M, C34, N36, 2KES, 2KHK） by the method of accelerated molecular dynamics （aMD） at room temperature with the implicit solvent model. Starting from the linear structures, these proteins successfully fold to the native structure in a lO0-ns aMD simulation. In contrast, they are failed under the traditional MD simulation in the same simulation time. Then we find that the lowest root mean square deviations of helix structures from the native structures are 0.36 A, 0.63 A, 0.52 A, 1.1 A and 0.78 A. What is more, native contacts, cluster and free energy analyses show that the results of the aMD method are in accordance with the experiment very well. All analyses show that the aMD can accelerate the simulation process, thus we may apply it to the field of computer aided drug designs.

A Novel Pre-control Method of Vehicle Dynamics Stability Based on Critical Stable Velocity during Transient Steering Maneuvering

The current research of direct yaw moment control（DYC） system focus on the design of target yaw moment and the distribution of wheel brake force. The differential braking intervention can effectively improve the lateral stability of the vehicle, however, the effect of DYC can be improved a step further by applying the control of vehicle longitudinal velocity. In this paper, the relationship between the vehicle longitudinal velocity and lateral stability is studied, and the simulation results show that a decrease of 5 km/h of longitudinal velocity at a particular situation can bring 100° increasing of stable steering upper limit. A critical stable velocity considering the effect of steering and yaw rate measurement is defined to evaluate the risk of losing steer-ability or stability. A novel velocity pre-control method is proposed by using a hierarchical pre-control logic and is integrated with the traditional DYC system. The control algorithm is verified through a hardware in-the-loop simulation system. Double lane change（DLC） test results on both high friction coefficient（μ） and low μ roads show that by using the pre-control method, the steering effort in DLC test can be reduced by 38% and 51% and the peak value of brake pressure control can be reduced by 20% and 12% respectively on high μ and low μ roads, the lateral stability is also improved. This research proposes a novel DYC system with lighter control effort and better control effect.

Spontaneous Recovery in Directed Dynamical Networks

Complex networked systems,which range from biological systems in the natural world to infrastructure systems in the human-made world,can exhibit spontaneous recovery after a failure;for example,a brain may spontaneously return to normal after a seizure,and traffic flow can become smooth again after a jam.Previous studies on the spontaneous recovery of dynamical networks have been limited to undirected networks.However,most real-world networks are directed.To fill this gap,we build a model in which nodes may alternately fail and recover,and we develop a theoretical tool to analyze the recovery properties of directed dynamical networks.We find that the tool can accurately predict the final fraction of active nodes,and the prediction accuracy decreases as the fraction of bidirectional links in the network increases,which emphasizes the importance of directionality in network dynamics.Due to different initial states,directed dynamical networks may show alternative stable states under the same control parameter,exhibiting hysteresis behavior.In addition,for networks with finite sizes,the fraction of active nodes may jump back and forth between high and low states,mimicking repetitive failure-recovery processes.These findings could help clarify the system recovery mechanism and enable better design of networked systems with high resilience.

A study on dynamic shear strength on frozen soil-concrete interface

Using newly developed dynamic shearing devices, the dynamic sheafing strength of frozen soil-conerete interface was studied experimentally. By placing concrete blocks in the lower half of the shear box and frozen soil sample in the upper part, a series of dynamic shear tests on their interfaces were carried out. The obtained results are summarized and the main influencing factors are revealed.

Robotic Knee Tracking Control to Mimic the Intact Human Knee Profile Based on Actor-Critic Reinforcement Learning

We address a state-of-the-art reinforcement learning(RL)control approach to automatically configure robotic pros-thesis impedance parameters to enable end-to-end,continuous locomotion intended for transfemoral amputee subjects.Specifically,our actor-critic based RL provides tracking control of a robotic knee prosthesis to mimic the intact knee profile.This is a significant advance from our previous RL based automatic tuning of prosthesis control parameters which have centered on regulation control with a designer prescribed robotic knee profile as the target.In addition to presenting the tracking control algorithm based on direct heuristic dynamic programming(dHDP),we provide a control performance guarantee including the case of constrained inputs.We show that our proposed tracking control possesses several important properties,such as weight convergence of the learning networks,Bellman(sub)optimality of the cost-to-go value function and control input,and practical stability of the human-robot system.We further provide a systematic simulation of the proposed tracking control using a realistic human-robot system simulator,the OpenSim,to emulate how the dHDP enables level ground walking,walking on different terrains and at different paces.These results show that our proposed dHDP based tracking control is not only theoretically suitable,but also practically useful.

Prompt efficiency of energy harvesting by magnetic coupling of an improved bi-stable system

In order to improve the transform efficiency of bi-stable energy harvester（BEH）,this paper proposes an advanced bi-stable energy harvester（ABEH）,which is composed of two bi-stable beams coupling through their magnets.Theoretical analyzes and simulations for the ABEH are carried out.First,the mathematical model is established and its dynamical equations are derived.The formulas of magnetic force in two directions are given.The potential energy barrier of ABEH is reduced and the snap-through is liable to occur between potential wells.To demonstrate the ABEH＇s advantage in harvesting energy,comparisons between the ABEH and the BEH are carried out for both harmonic and stochastic excitations.Our results reveal that the ABEH＇s inter-well response can be elicited by a low-frequency excitation and the harvester can attain frequent jumping between potential wells at fairly weak random excitations.Thus,it can generate a higher output power.The present findings prove that the ABEH is preferable in harvesting energy and can be optimally designed such that it attains the best harvesting performance.

Direct heuristic dynamic programming based on an improved PID neural network

In this paper, an improved PID-neural network （IPIDNN） structure is proposed and applied to the critic and action networks of direct heuristic dynamic programming （DHDP）. As one of online learning algorithm of approximate dynamic programming （ADP）, DHDP has demonstrated its applicability to large state and control problems. Theoretically, the DHDP algorithm requires access to full state feedback in order to obtain solutions to the Bellman optimality equation. Unfortunately, it is not always possible to access all the states in a real system. This paper proposes a solution by suggesting an IPIDNN configuration to construct the critic and action networks to achieve an output feedback control. Since this structure can estimate the integrals and derivatives of measurable outputs, more system states are utilized and thus better control performance are expected. Compared with traditional PIDNN, this configuration is flexible and easy to expand. Based on this structure, a gradient decent algorithm for this IPIDNN-based DHDP is presented. Convergence issues are addressed within a single learning time step and for the entire learning process. Some important insights are provided to guide the implementation of the algorithm. The proposed learning controller has been applied to a cart-pole system to validate the effectiveness of the structure and the algorithm.

Real-time trajectory planning for UCAV air-to-surface attack using inverse dynamics optimization method and receding horizon control

This paper presents a computationally efficient real-time trajectory planning framework for typical unmanned combat aerial vehicle （UCAV） performing autonomous air-to-surface （A/S） attack. It combines the benefits of inverse dynamics optimization method and receding horizon optimal control technique. Firstly, the ground attack trajectory planning problem is mathematically formulated as a receding horizon optimal control problem （RHC-OCP）. In particular, an approximate elliptic launch acceptable region （LAR） model is proposed to model the critical weapon delivery constraints. Secondly, a planning algorithm based on inverse dynamics optimization, which has high computational efficiency and good convergence properties, is developed to solve the RHCOCP in real-time. Thirdly, in order to improve robustness and adaptivity in a dynamic and uncer- tain environment, a two-degree-of-freedom （2-DOF） receding horizon control architecture is introduced and a regular real-time update strategy is proposed as well, and the real-time feedback can be achieved and the not-converged situations can be handled. Finally, numerical simulations demon- strate the efficiency of this framework, and the results also show that the presented technique is well suited for real-time implementation in dynamic and uncertain environment.

Ocean dynamic noise energy flux directivity in the 400 Hz to 700 Hz frequency band

Results of field studies of underwater dynamic noise energy flux directivity at two wind speeds, 6 m/s and 12 m/s, in the 400 Hz to 700 Hz frequency band in the deep open ocean are presented. The measurements were made by a freely drifting telemetric combined system at 500 m depth. Statistical characteristics of the horizontal and vertical dynamic noise energy flux directivity are considered as functions of wind speed and direction. Correlation between the horizontal dynamic noise energy flux direction and that of the wind was determined; a mechanism of the horizontal dynamic noise energy flux generation is related to the initial noise field scattering on ocean surface waves.

Density Functional Theory and Tight Binding-Based Dynamical Studies of Carbon Metal Systems of Relevance to Carbon Nanotube Growth

Density functional theory(DFT)and tight binding(TB)models have been used to study systems containing single-walled carbon nanotubes(SWNTs)and metal clusters that are of relevance to SWNT growth and regrowth.In particular,TB-based Monte Carlo(TBMC)simulations at 1000 or 1500 K show that Ni atoms that are initially on the surface of the SWNT or that are clustered near the SWNT end diffuse to the nanotube end so that virtually none of the Ni atoms are located inside the nanotube.This occurs,in part,due to the lowering of the Ni atom energies when they retract from the SWNT to the interior of the cluster.Aggregation of the atoms at the SWNT end does not change the chirality within the simulation time,which supports the application of SWNT regrowth(seeded growth)as a potential route for chirality-controlled SWNT production.DFT-based geometry optimisation and direct dynamics at 2000 K show that Cr and Mo atoms in Cr5Co50 and Mo5Co50 clusters prefer to be distributed in the interior of the clusters.Extension of these calculations should deepen our understanding of the role of the various alloy components in SWNT growth.

Experimental and numerical analysis of the heat flux characteristic of the plume of a 120-N thruster

The convective heat transfer of the plume of a 120-N thruster is investigated experimentally and numerically. Numerical results agree well with experimental results in that there is a nonlinear decrease in convective heat transfer with an increasing cone angle. It is also found that convective heat transfer decreases with increasing distance from the thruster outlet. Furthermore, the convective heat transfer of the plume mainly concentrates within a 35° cone angle and the heat flux decreases to the same order as solar radiation at the Earth’s surface when the cone angle exceeds 60°. The results of the study will help improve spacecraft design.

Partial Dynamic Dimension Reduction for Conditional Mean in Regression

In many regression analysis,the authors are interested in regression mean of response variate given predictors,not its the conditional distribution.This paper is concerned with dimension reduction of predictors in sense of mean function of response conditioning on predictors.The authors introduce the notion of partial dynamic central mean dimension reduction subspace,different from central mean dimension reduction subspace,it has varying subspace in the domain of predictors,and its structural dimensionality may not be the same point by point.The authors study the property of partial dynamic central mean dimension reduction subspace,and develop estimated methods called dynamic ordinary least squares and dynamic principal Hessian directions,which are extension of ordinary least squares and principal Hessian directions based on central mean dimension reduction subspace.The kernel estimate methods for dynamic ordinary least squares and dynamic Principal Hessian Directions are employed,and large sample properties of estimators are given under the regular conditions.Simulations and real data analysis demonstrate that they are effective.