Asymmetric Coupling Two-lane with Same Hopping Probabilities p Simple Exclusion Processes

AbstractIn the paper, we study a model which is that the particles move forward inside the two parallel lanes with the same hopping probabilities p (0<p<1) and jump only one direction between the both lanes by theoretical means. When the system is in the stationary-state, we can calculate currents, density profiles and phase diagrams by an approximate theoretical approach. The system includes seven stationary-state phases, there is an interesting phenomenon that when hopping probabilities become small, except the (MC, MC) phase, the region of all other phases decrease. This model can help us to understand the effect of traffic system when the speed of vehicles decreases better. Computer simulation is used to simulate the results. It can be found that the theoretical results are in excellent agreement with Monte Carlo computer simulations

Nanostructure toughening of epoxy thermosets with block Ionomer complexes

This thesis addresses an important issue in polymer materials science, the toughening of thermosetting polymers. A novel approach has been developed, i.e., the use of block ionomers/complexes to promote compatibilization with thermosetting epoxies. The morphology and mechanical properties of the resulting nanostructured epoxies were intensively studied to establish structure-property correlation

Numerical Simulation on Thermal Energy Storage Behavior of Cu/paraffin nanofluids PCMs

AbstractPCMs have foreseeable applications in residential buildings for effective use of solar energy. Paraffin is cheap and has moderate thermal energy storage density but low thermal conductivity. In this paper, we numerically investigate the melting processes of Cu/paraffin nanofluids PCMs. The results strongly suggested that the phase change heat transfer of paraffin was enhanced due to the addition of nanoparticles. For 1 wt% Cu/paraffin, the melting time can be saved 13.1%. The numerical results have a good agreement with the experimental results in describing the melting phenomena. These results show that adding nanoparticles is an efficient way to enhance the heat transfer in latent heat thermal energy storage system

Phase behavior and nanomechanical mapping of block ionomer complexes

Block ionomer complexes SSEBS-c-PCL were prepared, as a consequence of proton transfer from the sulfonic acid of sulfonated polystyrene-block- poly(ethylene-ran-butylene)-block-polystyrene (SSEBS) to the tertiary amine of a tertiary amine terminated poly(?-caprolactone) (APCL). The phase behavior of SSEBS-c-PCL was thoroughly investigated and the results showed that APCL in SSEBS-c-PCL displays unique crystallization behavior owing to the influence of interactions between the amine and sulfonic acid groups as well as the effects of confinement. Further, small-angle X-ray scattering study revealed that SSEBS-c-PCL displays a less ordered micro-phase structure compared to SSEBS. A quantitative mapping of mechanical properties at the nanoscale was achieved using peak force mode atomic force microscopy. It is found that the block ionomer complex possesses a higher average elastic modulus after complexation with crystallizable APCL. Additionally, the moduli for both hard and soft phases increase and the phase with higher modulus assignable to the hard SPS component shows much more pronounced changes after complexation, confirming that APCL interacts mainly with the SPS blocks. This provides an understanding of the composition and nanomechanical properties of these new block ionomer complexes and an alternative insight into the micro-phase structures of multi-phase materials

Mapping Soil Alkalinity and Salinity in Northern Songnen Plain, China with the HJ-1 Hyperspectral Imager Data and Partial Least Squares Regression

In arid and semi-arid regions, identifying and monitoring of soil alkalinity and salinity are in urgently need for preventing land degradation and maintaining ecological balances. In this study, physicochemical, statistical, and spectral analysis revealed that potential of hydrogen (pH) and electrical conductivity (EC) characterized the saline-alkali soils and were sensitive to the visible and near infrared (VIS-NIR) wavelengths. On the basis of soil pH, EC, and spectral data, the partial least squares regression (PLSR) models for estimating soil alkalinity and salinity were constructed. The R2 values for soil pH and EC models were 0.77 and 0.48, and the root mean square errors (RMSEs) were 0.95 and 17.92 dS/m, respectively. The ratios of performance to inter-quartile distance (RPIQ) for the soil pH and EC models were 3.84 and 0.14, respectively, indicating that the soil pH model performed well but the soil EC model was not considerably reliable. With the validation dataset, the RMSEs of the two models were 1.06 and 18.92 dS/m. With the PLSR models applied to hyperspectral data acquired from the hyperspectral imager (HSI) onboard the HJ-1A satellite (launched in 2008 by China), the soil alkalinity and salinity distributions were mapped in the study area, and were validated with RMSEs of 1.09 and 17.30 dS/m, respectively. These findings revealed that the hyperspectral images in the VIS-NIR wavelengths had the potential to map soil alkalinity and salinity in the Songnen Plain, China

Fault Detection and Diagnosis for Gas Turbines Based on a Kernelized Information Entropy Model

Gas turbines are considered as one kind of the most important devices in power engineering and have been widely used in power generation, airplanes, and naval ships and also in oil drilling platforms. However, they are monitored without man on duty in the most cases. It is highly desirable to develop techniques and systems to remotely monitor their conditions and analyze their faults. In this work, we introduce a remote system for online condition monitoring and fault diagnosis of gas turbine on offshore oil well drilling platforms based on a kernelized information entropy model. Shannon information entropy is generalized for measuring the uniformity of exhaust temperatures, which reflect the overall states of the gas paths of gas turbine. In addition, we also extend the entropy to compute the information quantity of features in kernel spaces, which help to select the informative features for a certain recognition task. Finally, we introduce the information entropy based decision tree algorithm to extract rules from fault samples. The experiments on some real-world data show the effectiveness of the proposed algorithms

A Density Peak-Based Clustering Approach for Fault Diagnosis of Photovoltaic Arrays

Fault diagnosis of photovoltaic (PV) arrays plays a significant role in safe and reliable operation of PV systems. In this paper, the distribution of the PV systems’ daily operating data under different operating conditions is analyzed. The results show that the data distribution features significant nonspherical clustering, the cluster center has a relatively large distance from any points with a higher local density, and the cluster number cannot be predetermined. Based on these features, a density peak-based clustering approach is then proposed to automatically cluster the PV data. And then, a set of labeled data with various conditions are employed to compute the minimum distance vector between each cluster and the reference data. According to the distance vector, the clusters can be identified and categorized into various conditions and/or faults. Simulation results demonstrate the feasibility of the proposed method in the diagnosis of certain faults occurring in a PV array. Moreover, a 1.8 kW grid-connected PV system with 6×3 PV array is established and experimentally tested to investigate the performance of the developed method

Relaxation oscillations of a piecewise-smooth slow-fast Bazykin's model with Holling type Ⅰ functional response

In this paper, we consider the dynamics of a slow-fast Bazykin's model with piecewise-smooth Holling type Ⅰ functional response. We show that the model has Saddle-node bifurcation and Boundary equilibrium bifurcation. Furthermore, it is also proven that the model has a homoclinic cycle, a heteroclinic cycle or two relaxation oscillation cycles for different parameters conditions. These results imply the dynamical behavior of the model is sensitive to the predator competition rate and the initial densities of prey and predators. In order to support the theoretical analysis, we present some phase portraits corresponding to different values of parameters by numerical simulation. These phase portraits include two relaxation oscillation cycles, an unstable relaxation oscillation cycle surrounded by a stable homoclinic cycle; the coexistence of a heteroclinic cycle and an unstable relaxation oscillation cycle. These results reveal far richer and much more complex dynamics compared to the model without different time scale or with smooth Holling type Ⅰ functional response