Enhancement of solid-liquid mixing state quality in a mechanical stirred reactor with serial-chaotic rotation generated by basic speed method.

In this work, a novel controllable chaotic stirring strategy that basic speed with chaotic mappings is proposed to enhance the solid-liquid mixing state quality. Specially, the modern statistical image analysis technique is introduced to explore the intensification mechanism of the mixing process. Results show that the best experimental conditions are obtained by studying the influence of factors such as the type of chaotic mapping, the speed change time, and the basic speed on the mixing state quality. Moreover, the case in which the basic speed is set to 150 r/min generated by the cascaded Logistic-Cubic chaotic mapping is the best while the speed change time is set to 5 s and the fluctuation threshold is 30. The mixing time of this case is 50 s and the shortest, energy consumption is 1.64 × 104 W/m3 and appropriate, the solid particle suspension quality is 83 and the best.

Quality control prediction of electrolytic copper using novel hybrid nonlinear analysis algorithm.

Traditional linear regression and neural network models demonstrate suboptimal fit and lower predictive accuracy while the quality of electrolytic copper is estimated. A more dependable and accurate model is essential for these challenges. Notably, the maximum information coefficient was employed initially to discern the non-linear correlation between the nineteen factors influencing electrolytic copper quality and the five quality control indicators. Additionally, the random forest algorithm elucidated the primary factors governing electrolytic copper quality. A hybrid model, integrating particle swarm optimization with least square support vector machine, was devised to predict electrolytic copper quality based on the nineteen factors. Concurrently, a hybrid model combining random forest and relevance vector machine was developed, focusing on primary control factors. The outcomes indicate that the random forest algorithm identified five principal factors governing electrolytic copper quality, corroborated by the non-linear correlation analysis via the maximum information coefficient. The predictive accuracy of the relevance vector machine model, when accounting for all nineteen factors, was comparable to the particle swarm optimization-least square support vector machine model, and surpassed both the conventional linear regression and neural network models. The predictive error for the random forest-relevance vector machine hybrid model was notably less than the sole relevance vector machine model, with the error index being under 5%. The intricate non-linear variation pattern of electrolytic copper quality, influenced by numerous factors, was unveiled. The advanced random forest-relevance vector machine hybrid model circumvents the deficiencies seen in conventional models. The findings furnish valuable insights for electrolytic copper quality management.

Numerical investigation of the effect of carotid bifurcation stenosis degree on pulsatility characteristics.

Arterial bifurcations are regions that are susceptible to hemodynamic effects and thrombus formation. In the current study, the hemodynamic effects of a simplified 3D model of an arterial bifurcation were simulated using the commercial computational fluid dynamics software FLUENT. The non-Newtonian properties of blood were modeled using the Carreau model, and the pulsation dynamics and heat transfer characteristics of blood at different degrees of stenosis in the arterial bifurcation were analyzed. The results indicate that arterial stenosis caused by a thrombus when the pulsation velocity reaches its peak has an essential impact on blood transport. The stenosis of the bifurcation increases the peak pulsatile flow pressure drop, and each 0.5 mm stenosis of the arterial bifurcation increases the mean wall shear stress of the bifurcated segment by approximately 0.25 Pa. From the heat transfer perspective, arterial stenosis has little effect on the heat transfer coefficient. The heat transfer coefficient measured inside the bifurcation is much larger than that measured outside the bifurcation. The stenosis of the arterial bifurcation causes an increase in the mean velocity of the arterial cross-section, and the volume-averaged absolute vorticity is introduced to quantify the secondary flow effect during the pulsation cycle, where the arterial stenosis causes an increase in the mean absolute vorticity at pulsation velocity and accelerates the decay of the vorticity at uniform velocity. In this paper, the hemodynamics of carotid bifurcation pulsation is analyzed in conjunction with flow field properties to reveal the flow field dynamics factors and heat transfer characteristics of local stenosis of the carotid bifurcation and to conduct an exploratory study for the diagnosis and treatment of carotid bifurcation thrombosis.

Identifying Flow Patterns in a Narrow Channel via Feature Extraction of Conductivity Measurements with a Support Vector Machine.

In this work, a visualization experiment for rectangular channels was carried out to explore gas-liquid two-phase flow characteristics. Typical flow patterns, including bubble, elastic and mixed flows, were captured by direct imaging technology and the corresponding measurements with fluctuation characteristics were recorded by using an electrical conductivity sensor. Time-domain and frequency-domain characteristics of the corresponding electrical conductivity measurements of each flow pattern were analyzed with a probability density function and a power spectral density curve. The results showed that the feature vectors can be constructed to reflect the time-frequency characteristics of conductivity measurements successfully by introducing the quantized characteristic parameters, including the maximum power of the frequency, the standard deviation of the power spectral density, and the range of the power distribution. Furthermore, the overall recognition rate of the four flow patterns measured by the method was 93.33% based on the support vector machine, and the intelligent two-phase flow-pattern identification method can provide a new technical support for the online recognition of gas-liquid two-phase flow patterns in rectangular channels. It may thus be concluded that this method should be of great significance to ensure the safe and efficient operation of relevant industrial production systems.

Quantifying the evolution of flow boiling bubbles by statistical testing and image analysis: toward a general model.

A new index, the estimate of the error variance, which can be used to quantify the evolution of the flow patterns when multiphase components or tracers are difficultly distinguishable, was proposed. The homogeneity degree of the luminance space distribution behind the viewing windows in the direct contact boiling heat transfer process was explored. With image analysis and a linear statistical model, the F-test of the statistical analysis was used to test whether the light was uniform, and a non-linear method was used to determine the direction and position of a fixed source light. The experimental results showed that the inflection point of the new index was approximately equal to the mixing time. The new index has been popularized and applied to a multiphase macro mixing process by top blowing in a stirred tank. Moreover, a general quantifying model was introduced for demonstrating the relationship between the flow patterns of the bubble swarms and heat transfer. The results can be applied to investigate other mixing processes that are very difficult to recognize the target.