Improved Model for Predicting Total Dissolved Gas Generation With the Residence Time of the Water in the Stilling Phase

Quantitative predictions of total dissolved gas (TDG) super-saturation are essential for developing operation schemes for high dams. Most TDG generation prediction models have various shortcomings that affect the accuracy of TDG super-saturation estimation, such as oversimplification of influencing factors and uncertainty in parameter values. In this study, the TDG generation process was divided into three parts, gas-liquid mass transfer process in the stilling phase, dilution resulting from the water jet plunging into the stilling phase, and outflow of TDG–super-saturated water from the stilling phase, while considering the water body and bubbles in the stilling phase as a whole. The residence time of the water in the stilling phase (Tr) was introduced to estimate mass transfer time, along with dimensional analysis methods. The properties of TDG generation were evaluated experimentally under varying Tr values. Based on the theoretical analysis and experimental results, a basic water renewal model was proposed and was validated using experimental data. Furthermore, prediction results of this model were compared with those of a classical empirical model and mechanical model based on observed data from a field survey at Xiluodu Dam. The results show that the relative errors between the predicted and experimental measurements were all less than 5%, indicating that the developed prediction model has a good performance. Compared with the mechanism model, the developed model could reduce the standard error (SE), normalized mean error (NME), and error of maximum (REMAX) by 60, 96, and 15%, respectively. Meanwhile, the developed model could reduce the SE, NME, REMAX by 17.4, 36, and 23%, respectively, compared with the empirical model. Considering all the error indexes, it can be concluded that the prediction performance of the water renewal model is the best among the three models. The proposed model was also more generically versatile than the existing models. Prediction results of water regeneration model for TDG could aid the drafting of governing strategies to minimize the risk of super-saturated TDG.

Renewal model for anomalous traffic in Internet2 links

We propose and estimate an alternating renewal model describing the propagation of anomalies in a backbone internet network in the United States. Internet anomalies, either caused by equipment malfunction, news events or malicious attacks, have been a focus of research in network engineering since the advent of the internet over 30 years ago. This article contributes to the understanding of statistical properties of the times between the arrivals of the anomalies, their duration and stochastic structure. Anomalous, or active, time periods are modelled as periods containing clusters or 1s, where 1 indicates a presence of an anomaly. The inactive periods consisting entirely of 0s dominate the 0–1 time series in every link. Since the active periods contain 0s, a separation parameter is introduced and estimated jointly with all other parameters of the model. Our statistical analysis shows that the integer-valued separation parameter and five other non-negative, scalar parameters satisfactorily describe all statistical properties of the observed 0–1 series.

Renewal model for anomalous traffic in Internet2 links

We propose and estimate an alternating renewal model describing the propagation of anomalies in a backbone internet network in the United States. Internet anomalies, either caused by equipment malfunction, news events or malicious attacks, have been a focus of research in network engineering since the advent of the internet over 30 years ago. This article contributes to the understanding of statistical properties of the times between the arrivals of the anomalies, their duration and stochastic structure. Anomalous, or active, time periods are modelled as periods containing clusters or 1s, where 1 indicates a presence of an anomaly. The inactive periods consisting entirely of 0s dominate the 0–1 time series in every link. Since the active periods contain 0s, a separation parameter is introduced and estimated jointly with all other parameters of the model. Our statistical analysis shows that the integer-valued separation parameter and five other non-negative, scalar parameters satisfactorily describe all statistical properties of the observed 0–1 series.

Experimental and Numerical Simulation Study of Devolatilization in a Self-Wiping Corotating Parallel Twin-Screw Extruder

Devolatilization is an important process for separating and removing unnecessary residual volatile substances or solvents during the production of polymers using twin-screw extruders. Latinen proposed a surface renewal model to determine the concentration of volatile components in the extrudate of a single-screw extruder. When a twin-screw extruder is used to calculate the concentration, it is necessary to use the exposed surface area of the resin in the starved region of Latinen’s model, which, however, is difficult to estimate. In our previous work, we numerically determined resin profiles of the screws using the 2.5D Hele–Shaw flow model and the finite element method, which helps in estimating the surface area of devolatilization. In this study, we numerically analyzed the volatile concentration of the extrudate in a self-wiping corotating twin-screw extruder using Latinen’s surface renewal model along with our resin profile calculation method. The experimental results of the concentrations of the volatile component (toluene) in the extrudate of polypropylene agreed well with its numerical calculation with a relative error of 6.5% (except for the data of the lowest rotational speed). Our results also showed that decreasing the flow rate and increasing the pump capacity were effective for removing the volatile component. The screw pitch of a full-flight screw was not affected by the devolatilization efficiency with a fixed flow rate and screw speed.