Optimal design and numerical analysis of multi-field coupling in a screw pump under wet gas compression.

During on-site operation under wet gas compression, the volumetric efficiency of a twin-screw pump decreases sharply, the temperature in the pump rises significantly, severe vibration and even jamming can occur. To solve the problems caused by wet gas compression structurally, a method for optimizing the design of a decompression screw is proposed in this paper based on the characteristics of a twin-screw pump and screw compressor. A fluid-solid multi-field thermal-coupling scheme is used to calculate the pressure, temperature, deformation, outflow, and volumetric efficiency of a screw pump before and after optimization. The average deviation between the calculated outflow and the experimental results was no more than 5%, demonstrating the accuracy of the calculation. After optimization, the return flow between the pump stages was significantly reduced and the outflow had increased. When the pressure difference between the inlet and outlet was 1 MPa, the maximum increase was about 18%. With an increase of the gas volume fraction from 95% to 99%, the average increase of the volumetric efficiency was about 14%. The temperature rise in the chambers at all levels and the temperature difference between the inlet and outlet of the decompression screw pump were lower than those of a traditional screw pump. The deformations at the three clearances were also less than those of a traditional screw pump, which helps to avoid jamming caused by thermal expansion.

Optimizing the Clearance Fit of a Progressive Cavity Pump for the Thermal Recovery of Petroleum Using Fluid-Structure Thermal Coupling.

It is important to incorporate the effects of fluid-structure thermal coupling and the boundary conditions when calculating the thermal dynamics and response of progressive cavity pumps. This study develops a fluid-structure thermal coupling model of the progressive cavity pump to improve the accuracy of its measured field of deformation and the temperature field of the stator. A function for the macroscopic motion of a rigid body is written in a typical fluid domain, and the user-defined function (UDF) in FLUENT is used to load the boundary of the rotor to realize its planetary motion. Local remeshing is used to update the moving grid, and the viscoelastic hysteretic heat of the stator is treated as the internal source of heat. One-way decoupling is used based on tire slip theory, and heat flux on the surface of the stator is calculated by the ANSYS Parametric Design Language (APDL). The sequential fluid-structure thermal coupling of the progressive cavity pump is calculated by using the ANSYS workbench. A scheme for optimizing the fit clearance of the stator and rotor is given to improve the volumetric efficiency of the progressive cavity pump and prevent the stator from being stuck owing to a large deformation, and the calculations are verified by experiments on volumetric efficiency. The average deviation in the calculated volumetric efficiency was less than 5% compared with the experimental values. The proposed scheme provides a theoretical basis for the optimal design of the progressive cavity pump for the thermal production of petroleum.

Grain Size Effects on Mechanical Properties of Nanocrystalline Cu6Sn5 Investigated Using Molecular Dynamics Simulation.

Intermetallic compounds (IMCs) are inevitable byproducts during the soldering of electronics. Cu6Sn5 is one of the main components of IMCs, and its mechanical properties considerably influence the reliability of solder joints. In this study, the effects of grain size (8-20 nm) on the mechanical properties (Young's modulus, yield stress, ultimate tensile strength (UTS), and strain rate sensitivity) of polycrystalline Cu6Sn5 were investigated using molecular dynamics simulations at 300 K and at a strain rate of 0.0001-10 ps-1. The results showed that at high strain rates, grain size only slightly influenced the mechanical properties. However, at low strain rates, Young's modulus, yield stress, and UTS all increased with increasing grain size, which is the trend of an inverse Hall-Petch curve. This is largely attributed to the sliding and rotation of grain boundaries during the nanoscale stretching process, which weakens the interaction between grains. Strain rate sensitivity increased with a decrease in grain size.

Maximizing the Field Emission Performance of Graphene Arrays.

To design efficient and powerful field emission cathodes, the screening effect is of great importance and should be traded off between screening and emitter number. It has long been found that to achieve maximum emission efficiency in an array, neighboring emitters are at two or three times their height from each other. However, this is only true for one-dimensional emitters, such as carbon nanotubes, but for graphene, a two-dimensional material, it is different. In this work, we found that to achieve maximum emission efficiency in an array of graphene, the separation of the emitter is four times the height, and it is insensitive to the anode voltage and the distance between the cathode and the anode.

Photo-induced enhanced negative absorption in the graphene-dielectric hybrid meta-structure.

Recently, the negative absorption in graphene-based metamaterials became a very attractive direction of THz electronic devices. Here we propose a graphene-dielectric hybrid meta-structure to realize photo-induced enhanced negative absorption in the THz regime, which results from strong graphene-light interaction. The negative absorption is derived from the degradation of the conductivity of graphene under optical pump. Meanwhile, the graphene-dielectric hybrid meta-structure introduces dispersion relation and resonance mode, which can couple with the incident wave to construct a strong resonance. In this case, both the dispersion of the propagating waves and resonance are contributed to the graphene-light interaction and enhance the negative absorption, in which the resonance coupling determines the distribution of negative absorption, and the maximum is dominated by dispersion. More importantly, compared with the previous work, the negative absorption is increased by nearly 100 times by adopting this meta-structure.

Spike-timing-dependent plasticity enhanced synchronization transitions induced by autapses in adaptive Newman-Watts neuronal networks.

In this paper, we numerically study the effect of spike-timing-dependent plasticity (STDP) on synchronization transitions induced by autaptic activity in adaptive Newman-Watts Hodgkin-Huxley neuron networks. It is found that synchronization transitions induced by autaptic delay vary with the adjusting rate Ap of STDP and become strongest at a certain Ap value, and the Ap value increases when network randomness or network size increases. It is also found that the synchronization transitions induced by autaptic delay become strongest at a certain network randomness and network size, and the values increase and related synchronization transitions are enhanced when Ap increases. These results show that there is optimal STDP that can enhance the synchronization transitions induced by autaptic delay in the adaptive neuronal networks. These findings provide a new insight into the roles of STDP and autapses for the information transmission in neural systems.

Synchronization transitions induced by the fluctuation of adaptive coupling strength in delayed Newman-Watts neuronal networks.

Introducing adaptive coupling in delayed neuronal networks and regulating the dissipative parameter (DP) of adaptive coupling by noise, we study the effect of fluctuations of the changing rate of adaptive coupling on the synchronization of the neuronal networks. It is found that time delay can induce synchronization transitions for intermediate DP values, and the synchronization transitions become strongest when DP is optimal. As the intensity of DP noise is varied, the neurons can also exhibit synchronization transitions, and the phenomenon is delay-dependent and is enhanced for certain time delays. Moreover, the synchronization transitions change with the change of DP and become strongest when DP is optimal. These results show that randomly changing adaptive coupling can considerably change the synchronization of the neuronal networks, and hence could play a crucial role in the information processing and transmission in neural systems.

Autaptic activity-induced synchronization transitions in Newman-Watts network of Hodgkin-Huxley neurons.

In this paper, we numerically study the effect of autapse on the synchronization of Newman-Watts small-world Hodgkin-Huxley neuron network. It is found that the neurons exhibit synchronization transitions as autaptic self-feedback delay is varied, and the phenomenon becomes strongest when autaptic self-feedback strength is optimal. This phenomenon also changes with the change of coupling strength and network randomness and become strongest when they are optimal. There are similar synchronization transitions for electrical and chemical autapse, but the synchronization transitions for chemical autapse occur more frequently and are stronger than those for electrical synapse. The underlying mechanisms are briefly discussed in quality. These results show that autaptic activity plays a subtle role in the synchronization of the neuronal network. These findings may find potential implications of autapse for the information processing and transmission in neural systems.

Adaptive coupling optimized spiking coherence and synchronization in Newman-Watts neuronal networks.

In this paper, we have numerically studied the effect of adaptive coupling on the temporal coherence and synchronization of spiking activity in Newman-Watts Hodgkin-Huxley neuronal networks. It is found that random shortcuts can enhance the spiking synchronization more rapidly when the increment speed of adaptive coupling is increased and can optimize the temporal coherence of spikes only when the increment speed of adaptive coupling is appropriate. It is also found that adaptive coupling strength can enhance the synchronization of spikes and can optimize the temporal coherence of spikes when random shortcuts are appropriate. These results show that adaptive coupling has a big influence on random shortcuts related spiking activity and can enhance and optimize the temporal coherence and synchronization of spiking activity of the network. These findings can help better understand the roles of adaptive coupling for improving the information processing and transmission in neural systems.

Periodic coupling strength-dependent multiple coherence resonance by time delay in Newman-Watts neuronal networks.

Recently, multiple coherence resonance induced by time delay has been observed in neuronal networks with constant coupling strength. In this paper, by employing Newman-Watts Hodgkin-Huxley neuron networks with time-periodic coupling strength, we study how the temporal coherence of spiking behavior and coherence resonance by time delay change when the frequency of periodic coupling strength is varied. It is found that delay induced coherence resonance is dependent on periodic coupling strength and increases when the frequency of periodic coupling strength increases. Periodic coupling strength can also induce multiple coherence resonance, and the coherence resonance occurs when the frequency of periodic coupling strength is approximately multiple of the spiking frequency. These results show that for periodic coupling strength time delay can more frequently optimize the temporal coherence of spiking activity, and periodic coupling strength can repetitively optimize the temporal coherence of spiking activity as well. Frequency locking may be the mechanism for multiple coherence resonance induced by periodic coupling strength. These findings imply that periodic coupling strength is more efficient for enhancing the temporal coherence of spiking activity of neuronal networks, and thus it could play a more important role in improving the time precision of information processing and transmission in neural networks.

Influence of time delay and channel blocking on multiple coherence resonance in Hodgkin-Huxley neuron networks.

Toxins such as tetraethylammonium (TEA) and tetrodotoxin (TTX) may reduce the number of working potassium and sodium ion channels by poisoning and making them blocked, respectively. In this paper, we study how channel blocking (CB) affects the time delay-induced multiple coherence resonance (MCR), i.e., a phenomenon that the spiking of neuronal networks intermittently reaches the most ordered state, in stochastic Hodgkin-Huxley neuron networks. It is found that potassium and sodium CB have distinct effects. For potassium CB, the MCR occurs more frequently as the CB develops, but for sodium CB the MCR is badly impaired and only the first coherence resonance (CR) holds and, consequently, the MCR evolves into a single CR as sodium CB develops. We found for sodium CB the spiking becomes disordered at larger delay lengths, which may be the reason for the destruction of the MCR. The underlying mechanism is briefly discussed in terms of distinct effects of potassium and sodium CB on the spiking activity. These results show that potassium CB can increase the frequency of MCR with time delay, but sodium CB may suppress and even destroy the delay-induced MCR. These findings may help to understand the joint effects of CB and time delay on the spiking coherence of neuronal networks.

Non-Gaussian noise-optimized intracellular cytosolic calcium oscillations.

We have numerically studied the effect of a particular kind of non-Gaussian colored noise (NGN), characterized by the deviation q from Gaussian noise (q=1), on intracellular cytosolic calcium (Ca(2+)) oscillations. It is found that, as q is increased, the Ca(2+) oscillation regularity increases and reaches a best performance at an optimal q, and then decreases with further increasing q, which represents the occurrence of coherence resonance, i.e., the most regular Ca(2+) oscillations. Similar phenomena occur for different values of noise intensity and correlation time of the NGN. This phenomenon of deviation-optimized Ca(2+) oscillations show that, external non-Gaussian noises of different types can enhance and even optimize the intracellular Ca(2+) oscillations. This result provides new insights into the constructive roles and potential applications of non-Gaussian noises in intracellular cytosolic Ca(2+) oscillations.

Multiple coherence resonance induced by time-periodic coupling in stochastic Hodgkin-Huxley neuronal networks.

In this paper, we study the effect of time-periodic coupling strength (TPCS) on the spiking coherence of Newman-Watts small-world networks of stochastic Hodgkin-Huxley (HH) neurons and investigate the relations between the coupling strength and channel noise when coherence resonance (CR) occurs. It is found that, when the amplitude of TPCS is varied, the spiking induced by channel noise can exhibit CR and coherence bi-resonance (CBR), and the CR moves to a smaller patch area (bigger channel noise) when the amplitude increases; when the frequency of TPCS is varied, the intrinsic spiking can exhibit CBR and multiple CR, and the CR always occurs when the frequency is equal to or multiple of the spiking period, manifesting as the locking between the frequencies of the intrinsic spiking and the coupling strength. These results show that TPCS can greatly enhance and optimize the intrinsic spiking coherence, and favors the spiking with bigger channel noise to exhibit CR. This implies that, compared to constant coupling strength, TPCS may play a more efficient role for improving the time precision of the information processing in stochastic neuronal networks.

Synchronization transitions on complex thermo-sensitive neuron networks with time delays.

We have numerically studied the firing synchronization transitions on random thermo-sensitive neuron networks in dependence on information transmission delay tau, network randomness p, and coupling strength g. It is found that as tau is increased the neurons can exhibit transitions from burst synchronization (BS) to clustering anti-phase synchronization (APS), and further to spike synchronization (SS). It is also found that, with increasing p or g, there are transitions from spatiotemporal chaos to BS, then to APS, and finally to SS. However, the APS state with p or g exists only for intermediate tau values within a narrow range. For tau values outside this range, the APS state does not appear and the firings change directly from spatiotemporal chaos to BS or SS. These results show that, as time delay can do, network topology and coupling strength can also cause complex synchronization transitions in the neurons. In particular, the novel phenomenon of APS state with p or g shows that, with the help of appropriate random connections or coupling strength, the neurons may exhibit the APS behavior at a certain time delay for which the APS does not appear originally. These findings imply that time delay, network randomness, and coupling strength may have subtle effects on the firing behaviors on neuronal networks, and thus could play important roles in the information processing in neural systems.

Non-Gaussian noise optimized spiking activity of Hodgkin-Huxley neurons on random complex networks.

In this paper, we numerically study how the NGN's deviation q from Gaussian noise (q=1) affects the spike coherence and synchronization of 60 coupled Hodgkin-Huxley (HH) neurons driven by a periodic sinusoidal stimulus on random complex networks. It is found that the effect of the deviation depends on the network randomness p (the fraction of random shortcuts): for larger p (p>0.15), the spiking regularity keeps being improved with increasing q; while, for smaller p (p< 0.15), the spiking regularity can reach the best performance at an optimal intermediate q value, indicating the occurrence of "deviation-optimized spike coherence". The synchronization becomes enhanced with decreasing q, and the enhancing extent for a random HH neuron network is stronger than for a regular one. These behaviors show that the spike coherence and synchronization of the present HH neurons on random networks can be more strongly enhanced by various other types of external noise than by Gaussian noise, whereby the neuron firings may behave more periodically in time and more synchronously in space. Our results provide the constructive roles of the NGN on the spiking activity of the present system of HH neuron networks.

Coherence resonance induced by the deviation of non-Gaussian noise in coupled Hodgkin-Huxley neurons.

Neurons are noisy elements. Noise arises from both intrinsic and extrinsic sources. In this paper, we numerically study the effect of a particular kind of colored non-Gaussian noise (NGN), mainly of its deviation q from Gaussian noise, on the collective firing in bidirectionally coupled deterministic Hodgkin-Huxley neurons. It is found that the coefficient of variation (CV), characterizing the temporal regularity of the collective spikes, nonlinearly changes with increasing q and passes through a minimum at an intermediate optimal q where the collective spiking becomes most regular, which represents the presence of coherence resonance (CR). We also present a global view of CV as a function of q and neuron number N under various appropriate values of noise intensity. For each value of noise intensity, there is an island present in the contour plot, which sufficiently demonstrates the phenomenon of "q-induced CR." This phenomenon, termed as q-induced CR, shows that there is an optimal deviation of the NGN by which the coupled neurons may behave most periodically in time. Our results provide a novel constructive role of the deviation of the NGN in information processing and signal transduction in real neural systems.

Structural, electronic, and magnetic properties of Fe3C2 cluster.

On the basis of density-functional theory and all-electron numerical basis set, 20 stable isomers of Fe(3)C(2) cluster are found through optimization calculations and frequency analysis from 108 initial structures. A nonplanar C(s) structure with nonet spin multiplicity and 482.978 kcal/mol of binding energy is found as the candidate of global minimum geometry of Fe(3)C(2) cluster. The binding energies, the energy gaps between the highest occupied molecular orbital and the lowest unoccupied molecular orbital, and the magnetic moments of all the isomers are reported. The relationship between the molecular properties and geometrical structures is also investigated.

Enhancement of stochastic oscillations by colored noise or internal noise in NO reduction by CO on small platinum surfaces.

In this paper, based on the stochastic model of NO reduction by CO on Pt crystal surfaces and taking Gaussian colored noise as external fluctuations of the NO partial pressure, we study the effect of the colored noise on the internal noise-induced stochastic oscillations (INSOs) and the effect of internal noise on the colored noise-induced stochastic oscillations (CNSOs). It is found that the INSO can be enhanced by the colored noise with appropriate correlation time or noise strength and, interestingly, the CNSO can be enhanced by the internal noise as well and, moreover, the enhanced CNSO can reach the best oscillatory states repetitively via proper internal noises. This effect of the internal noise is different from its effect on the stochastic oscillations induced by the external Gaussian white noise, which probably results from the interaction of the correlated colored noise and the internal noise.

Ordering spatiotemporal chaos in complex thermosensitive neuron networks.

We have studied the effect of random long-range connections in chaotic thermosensitive neuron networks with each neuron being capable of exhibiting diverse bursting behaviors, and found stochastic synchronization and optimal spatiotemporal patterns. For a given coupling strength, the chaotic burst-firings of the neurons become more and more synchronized as the number of random connections (or randomness) is increased and, rather, the most pronounced spatiotemporal pattern appears for an optimal randomness. As the coupling strength is increased, the optimal randomness shifts towards a smaller strength. This result shows that random long-range connections can tame the chaos in the neural networks and make the neurons more effectively reach synchronization. Since the model studied can be used to account for hypothalamic neurons of dogfish, catfish, etc., this result may reflect the significant role of random connections in transferring biological information.