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L. F. Wang - One of the best experts on this subject based on the ideXlab platform.
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Temporal evolution of bubble tip Velocity in classical Rayleigh-Taylor instability at arbitrary Atwood numbers
Physics of Plasmas, 2013Co-Authors: Wanhai Liu, L. F. WangAbstract:In this research, the temporal evolution of the bubble tip Velocity in Rayleigh-Taylor instability (RTI) at arbitrary Atwood numbers and different initial Perturbation velocities with a discontinuous profile in irrotational, incompressible, and inviscid fluids (i.e., classical RTI) is investigated. Potential models from Layzer [Astrophys. J. 122, 1 (1955)] and Perturbation Velocity potentials from Goncharov [Phys. Rev. Lett. 88, 134502 (2002)] are introduced. It is found that the temporal evolution of bubble tip Velocity [u(t)] depends essentially on the initial Perturbation Velocity [u(0)]. First, when the u(0)
Wanhai Liu - One of the best experts on this subject based on the ideXlab platform.
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Temporal evolution of bubble tip Velocity in classical Rayleigh-Taylor instability at arbitrary Atwood numbers
Physics of Plasmas, 2013Co-Authors: Wanhai Liu, L. F. WangAbstract:In this research, the temporal evolution of the bubble tip Velocity in Rayleigh-Taylor instability (RTI) at arbitrary Atwood numbers and different initial Perturbation velocities with a discontinuous profile in irrotational, incompressible, and inviscid fluids (i.e., classical RTI) is investigated. Potential models from Layzer [Astrophys. J. 122, 1 (1955)] and Perturbation Velocity potentials from Goncharov [Phys. Rev. Lett. 88, 134502 (2002)] are introduced. It is found that the temporal evolution of bubble tip Velocity [u(t)] depends essentially on the initial Perturbation Velocity [u(0)]. First, when the u(0)
Asbjorn Klomp - One of the best experts on this subject based on the ideXlab platform.
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Perturbation Velocity affects linearly estimated neuromechanical wrist joint properties
Journal of Biomechanics, 2018Co-Authors: Asbjorn Klomp, Jurriaan H De Groot, Hans J Arendzen, Carel G. M. Meskers, Erwin De Vlugt, Frans C T Van Der HelmAbstract:Abstract The dynamic behavior of the wrist joint is governed by nonlinear properties, yet applied mathematical models, used to describe the measured input-output (Perturbation-response) relationship, are commonly linear. Consequently, the linearly estimated model parameters will depend on properties of the applied Perturbation properties (such Perturbation amplitude and Velocity). We aimed to systematically address the effects of Perturbation Velocity on linearly estimated neuromechanical parameters. Using a single axis manipulator ramp and hold Perturbations were applied to the wrist joint. Effects of Perturbation Velocity (0.5, 1 and 3 rad/s) were investigated at multiple background torque levels (0, 0.5 and 1 N·m). With increasing Perturbation Velocity, estimated joint stiffness remained constant, while damping and reflex gain decreased. This variation in model parameters is dependent on background torque levels, i.e. muscle contraction. These observations support the future development of nonlinear models that are capable of describing wrist joint behavior over a larger range of loading conditions, exceeding the restricted range of operation that is required for linearization.
F Sergi - One of the best experts on this subject based on the ideXlab platform.
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effects of Perturbation Velocity direction background muscle activation and task instruction on long latency responses measured from forearm muscles
bioRxiv, 2020Co-Authors: J Weinman, P A Fatollahkhani, A Zonnino, R Nikonowicz, F SergiAbstract:The centeral nervous system uses feedback processes that occur at multiple time scales to control interactions with the environment. Insight on the neuromechanical mechanisms subserving the faster feedback processes can be gained by applying rapid mechanical Perturbations to the limb, and observing the ensuing muscle responses using electromyography (EMG). The long-latency response (LLR) is the fastest process that directly involve cortical areas, with a motorneuron response measurable 50 ms following an imposed limb displacement. Several behavioral factors concerning Perturbation mechanics and the active role of muscles prior or during the Perturbation can modulate the long-latency response amplitude (LLRa) in the upper limbs, but the interaction between many of these factors had not been systematically studied before. We conducted a behavioral study on thirteen healthy individuals to determine the effect and interaction of four behavioral factors -- background muscle torque, Perturbation direction, Perturbation Velocity, and task instruction -- on the LLRa evoked from the flexor carpi radialis (FCR) and extensor carpi ulnaris (ECU) muscles following the application of wrist displacements. The effects of the four factors listed above were quantified using both a 0D statistical analysis on the average Perturbation-evoked EMG signal in the period corresponding to an LLR, and using a timeseries analysis of EMG signals. All factors significantly modulated LLRa, and that their combination nonlinearly contributed to modulating the LLRa. Specifically, all the three-way interaction terms that could be computed without including the interaction between instruction and Velocity significantly modulated the LLR. Analysis of the three-way interaction terms of the 0D model indicated that for the ECU muscle, the LLRa evoked when subjects are asked to maintain their muscle activation in response to the Perturbations (DNI) was greater than the one observed when subjects yielded (Y) to the Perturbations ({Delta}LLRa -- DNI vs. Y: 1.76{+/-}0.16 nu, p<0.001), but this effect was not measured for muscles undergoing shortening or in absence of background muscle activation. Moreover, higher Perturbation Velocity increased the LLRa evoked from the stretched muscle in presence of a background torque ({Delta}LLRa 200-125 deg/s: 0.94{+/-}0.20 nu, p<0.001; {Delta}LLRa 125-50 deg/s: 1.09 {+/-}0.20 nu, p<0.001), but no effects of Velocity were measured in absence of background torque, nor effects of any of those factors was measured on muscles shortened by the Perturbations. The time-series analysis indicated the significance of some effects in the LLR region also for muscles undergoing shortening. As an example, the interaction between torque and instruction was significant also for the ECU muscle undergoing shortening, in part due to the composition of a positive and negative modulation of the response due to the interaction between of the two terms. The absence of a nonlinear interaction between task instruction and Perturbation Velocity suggest that the modulation introduced by these two factors are processed by distinct neural pathways.
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Effects of Perturbation Velocity, Direction, Background muscle activation, and Task Instruction on Long-Latency Responses Measured from Forearm Muscles
2020Co-Authors: J Weinman, P A Fatollahkhani, A Zonnino, R Nikonowicz, F SergiAbstract:The centeral nervous system uses feedback processes that occur at multiple time scales to control interactions with the environment. Insight on the neuromechanical mechanisms subserving the faster feedback processes can be gained by applying rapid mechanical Perturbations to the limb, and observing the ensuing muscle responses using electromyography (EMG). The long-latency response (LLR) is the fastest process that directly involve cortical areas, with a motorneuron response measurable 50 ms following an imposed limb displacement. Several behavioral factors concerning Perturbation mechanics and the active role of muscles prior or during the Perturbation can modulate the long-latency response amplitude (LLRa) in the upper limbs, but the interaction between many of these factors had not been systematically studied before. We conducted a behavioral study on thirteen healthy individuals to determine the effect and interaction of four behavioral factors -- background muscle torque, Perturbation direction, Perturbation Velocity, and task instruction -- on the LLRa evoked from the flexor carpi radialis (FCR) and extensor carpi ulnaris (ECU) muscles following the application of wrist displacements. The effects of the four factors listed above were quantified using both a 0D statistical analysis on the average Perturbation-evoked EMG signal in the period corresponding to an LLR, and using a timeseries analysis of EMG signals. All factors significantly modulated LLRa, and that their combination nonlinearly contributed to modulating the LLRa. Specifically, all the three-way interaction terms that could be computed without including the interaction between instruction and Velocity significantly modulated the LLR. Analysis of the three-way interaction terms of the 0D model indicated that for the ECU muscle, the LLRa evoked when subjects are asked to maintain their muscle activation in response to the Perturbations (DNI) was greater than the one observed when subjects yielded (Y) to the Perturbations ({Delta}LLRa -- DNI vs. Y: 1.76{+/-}0.16 nu, p
Kangning Li - One of the best experts on this subject based on the ideXlab platform.
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heat transfer enhancement through control of added Perturbation Velocity in flow field
Energy Conversion and Management, 2013Co-Authors: Jiansheng Wang, Cui Wu, Kangning LiAbstract:Abstract The characteristics of heat transfer and flow, through an added Perturbation Velocity, in a rectangle channel, are investigated by Large Eddy Simulation (LES). The downstream, vertical, and upstream control strategy, which can suppress the lift of low speed streaks in the process of improving the performance of heat transfer, are adopted in numerical investigation. Taking both heat transfer and flow properties into consideration, the synthesis performance of heat transfer and flow of three control strategies are evaluated. The numerical results show that the flow structure in boundary layer has been varied obviously for the effect of Perturbation Velocity and induced quasi-streamwise vortices emerging around the controlled zone. The results indicate that the vertical control strategy has the best synthesis performance of the three control strategies, which also has the least skin frication coefficient. The upstream and downstream strategies can improve the heat transfer performance, but the skin frication coefficient is higher than that with vertical control strategy.