Haijun Chen

The high separation efficiency of counter-current chromatography (CCC) strongly depends on the robust retention of the stationary phase, which is maintained by a hydrodynamic equilibrium between the two liquid phases. However, developing reliable computational fluid dynamics (CFD) models to simulate this process is challenging, mainly because the hydrodynamic mechanisms governing the equilibrium has not been fully clarified. The present study proposes an innovative modeling strategy based on the concept of a relatively stationary spiral tube in a rotational force field. Establishing this CFD model offers a novel approach to effectively simulate the two-phase hydrodynamic equilibrium process inherent to CCC. The simulation results indicate that the synergistic interaction between the rotational inertial force and the unidirectional spiral force is a necessary condition for achieving stationary phase retention. Based on this foundation, this study proposes a mechanism of stationary phase retention centered on three fluid behaviors: radial stratification, head-tail driving, and cyclic compensation. Furthermore, a concise retention criterion is established: an effective two-phase equilibrium process requires a force field exhibiting "two effects" and must strictly comply with correct operational rules. To validate this criterion, a spiral-disc apparatus was designed and constructed. Experimental results unequivocally confirm that strict adherence to this criterion enables the apparatus to achieve a stationary phase retention factor as high as 88.6%, even at relatively low rotational speeds. Conversely, under non-compliant conditions, the retention factor exhibits a significant decline, reaching approximately 15%. This work provides, for the first time, a clear elucidation of the hydrodynamic mechanism governing stationary phase retention in CCC. Furthermore, it establishes a novel theoretical framework and proposes a technical pathway for the design of next-generation high-performance CCC equipment.

The final-stage long blade of the steam turbine is selected as the research subject, and its strength performance and vibration characteristics are analyzed. A multi-objective parameter optimization is conducted, with blade tension height (P1), tension angle (P2), and perimeter band thickness (P3) as the design variables. The equivalent force and maximum deformation are set as constraints, while the resonance frequency at each of the “three key points” is defined as the objective function. The structural parameters are optimized using response surface methodology and the multi-objective genetic algorithm (MOGA). Numerical results show that the optimal parameter set is P1 = 804.1 mm, P2 = 8.91°, and P3 = 20.9 mm. After optimization, the avoidance rate for the intersection of the second-order three pitch diameter curve with the frequency multiplier curve ( K = 3) increases to 10%, a 3.4% improvement compared to pre-optimization. Similarly, the avoidance rate for the intersection of the third-order seven pitch diameter curve with the frequency multiplier curve ( K = 7) improves to −6.1%, reflecting a 3.43% increase over pre-optimization. This effectively mitigates the resonance risk associated with turbine blades, thereby enhancing turbine efficiency and stability, and offering a significant reference for turbine blade design optimization.

Welding stands as a critical focus for the intelligent and digital transformation of the machinery industry, with automated laser welding playing a pivotal role in the sector’s technological advancement. The management of welding deformation in such operations is fundamental, relying on advanced analysis and prediction methods. The endeavor to accurately analyze welding deformation in practical applications is compounded by the interplay of numerous variables, a pronounced coupling effect among these factors, and a reliance on expert intuition. Thus, effective deformation control in automated laser welding operations necessitates the gathering of pre-test laser welding data to develop a predictive approach that accurately reflects real-world conditions and is characterized by improved reliability and stability. To address the technological evolution in automated laser welding, a predictive model based on neural network technology is proposed to map the intricate relationship between process variables and the resulting deformation. At the heart of this approach is the formulation of a predictive model utilizing a back-propagation neural network (BP), with an emphasis on four essential welding parameters: speed, peak power, duty cycle, and defocusing amount. The model’s predictive accuracy is then honed through the application of the whale optimization algorithm (WOA) and the differential evolutionary (DE) algorithm. Finally, extensive testing in an automated laser welding experimental setup is conducted to validate the accuracy and reliability of the proposed prediction model. It is demonstrated through these experiments that the deformation prediction model, enhanced by the DEWOA-BP neural network, accurately forecasts the relationship between laser welding parameters and the induced deformation, maintaining a prediction error margin of ±0.1mm. The model is employed to fulfill the requirements for a pre-welding quality evaluation, thereby facilitating a more calculated and informed approach to welding operations. This method of intelligent prediction is not only crucial for the intelligent transformation of laser welding but also holds significant implications for traditional machining technologies such as milling, grinding, and spraying. It offers innovative ideas and methods that are pivotal for the industrial revolution and technological advancement of the traditional machining industry.