The squeeze film damper (SFD) is a damping device that is widely used in turbomachinery. A well-designed SFD structure can effectively mitigate vibration at critical speeds, while a poorly designed SFD may increase vi...
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The squeeze film damper (SFD) is a damping device that is widely used in turbomachinery. A well-designed SFD structure can effectively mitigate vibration at critical speeds, while a poorly designed SFD may increase vibration. The comprehensive multi-parameteroptimization method is used for optimizing the vibration response at critical speed and improving the reliability and overall performance by designing the crucial structural parameters of the SFD. Firstly, the coupling dynamic model is proposed for the rotor and SFD, taking into account the influence of non-linear oil film forces of SFD. The test rig is used to verify the vibration response in a typical SFD-rotor system. To provide the optimal vibration reduction effect within a specific SFD parameter range for a particular rotor, a method for comprehensive multi-parameteroptimization is introduced. This method introduces the hunter-prey intelligent optimization (HPO) algorithm and compares its results with the PSO algorithm. The comprehensive optimization method revealed that the key parameters of the SFD, when designed using this approach, can effectively alleviate the vibration response of the entire rotor system, achieving the rotor system amplitude reduction ratio of up to13.89%.
In this paper, a novel stochastic gradient particle swarm optimization (SGPSO) algorithm is proposed, which combines the high -efficiency of gradient search with the randomness of particle swarm search. By adjusting t...
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In this paper, a novel stochastic gradient particle swarm optimization (SGPSO) algorithm is proposed, which combines the high -efficiency of gradient search with the randomness of particle swarm search. By adjusting the stochastic gradient obtained by the best historical positions of adjacent two generations, the proposed algorithm can effectively overcome the problems of premature convergence and poor accuracy of standard particle swarm optimization (PSO). Due to the capabilities of rapidity, optimality and adaptability, the proposed algorithm is applied as a global optimization approach to rapidly generate feasible and smooth entry trajectories for hypersonic glide vehicles with highly constraints. Under the constraints of Earth's rotation and oblateness, the entry trajectory planning model is established. By parameterizing the control variables including angle of attack (AOA) and bank angle, the entry trajectory optimization problem is then converted to a multi-parameteroptimization problem, which can be solved by the proposed SGPSO algorithm. Considering Common Aero Vehicle (CAV) model, the simulations show that the proposed algorithm has better performance on optimization speed, stability and solution optimality than those of the classical methods, and it can realize the rapid optimization of entry trajectory for hypersonic glide vehicles. (C) 2018 Elsevier Masson SAS. All rights reserved.
Accurate extraction of weak feature information in strong background noise is a key to detect and identify rolling bearing faults. Stochastic resonance (SR) and vibrational resonance (VR) have received extensive atten...
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Accurate extraction of weak feature information in strong background noise is a key to detect and identify rolling bearing faults. Stochastic resonance (SR) and vibrational resonance (VR) have received extensive attention and research in weak signal detection by reason of their advantages of utilizing additional inputs (i.e. noise or high frequency harmonic signals) to enhance weak signals. Considering the advantages and disadvantages of SR and VR in weak signal detection, this paper combines the two to construct a cascaded feedback model of VR and SR, and utilize it to form a parallel resonance system, which improves the detection performance of weak signals through the ensemble average effect. Furthermore, a multi-parameteroptimization strategy based on the improved whale optimization algorithm (WOA) is proposed for the parameter selection of the parallel resonance system. It uses the constructed measurement index independent of the prior knowledge as the fitness function to realize automatic adjustment of multi-parameter, and obtains the final output by weighted summation of the optimal results obtained by multiple iterations. Finally, the suggested method is analyzed by numerical simu-lation signal and experimental data of rolling bearings, and the effectiveness and superiority of the proposed method in the detection of weak fault features are verified.
The rapid expansion of data centers and cloud computing has exacerbated the issue of high energy consumption and localized overheating in servers. Consequently, the identification of effective cooling methods and reso...
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The rapid expansion of data centers and cloud computing has exacerbated the issue of high energy consumption and localized overheating in servers. Consequently, the identification of effective cooling methods and resource allocation strategies for server rooms has become pivotal in enhancing overall airflow distribution and optimizing thermal performance within data centers. Time-efficient Computational Fluid Dynamics (CFD) tools offer an alternative to resourceintensive experimental measurements. However, it is difficult to monitor the information in real time and optimize the design of DC. In this study, an innovative application of Response Surface Methodology (RSM) is introduced to prediction thermal environment information and optimization performance of data center. In contrast to traditional single-factor optimization methods, this approach incorporates multiple factors, such as server workloads and air supply conditions, as optimizationparameters. Furthermore, maximum server temperatures, temperature differences, and other metrics are defined as key performance indicators (KPIs). A predictive model was developed with the aim of offering comprehensive information about the thermal environment of the data center. Moreover, the study qualitatively analyzes the influence of each parameter on various indicators. Fitting equations are solved based on actual conditions to determine optimized configuration schemes in real time for the entire data center or specific scenarios. This approach effectively reduces cooling energy consumption and optimizes data center thermal management. The optimized configuration resulted in a significant reduction of approximately 30% in the maximum server temperature and approximately 20% in the temperature difference. Additionally, the optimization methodology established in this study facilitates the implementation of a real-time control mechanism for data center cooling systems, enabling energy demand and management optimization. T
With the development of data center, air based cooling method should be updated due to the high energy consumption. Therefore, finding a high efficient cooling method becomes more and more urgent. Single-phase immersi...
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With the development of data center, air based cooling method should be updated due to the high energy consumption. Therefore, finding a high efficient cooling method becomes more and more urgent. Single-phase immersion cooling has received more attentions due to the low energy consumption. To improve the perfor-mance, more parameters and their interactive impacts should be considered. Therefore, this paper used seven key parameters to optimize server performance by using single-phase immersion cooling method, including fin height (A), fin spacing (B), thermal conductivity (C), height ratio of baffle (D), outlet area (E), inlet flowrate (F), and inlet temperature (G). The maximum temperature and pressure loss are used as the performance indicators. With the validated CFD model and response surface method (RSM), the impact of single parameter, multi -parameter, and weights of maximum temperature and pressure loss are analyzed. Results showed that, the regression equations for maximum temperature and pressure loss can well predict and optimize the server performance. In addition to the impact of single parameter, the interactive impact between height ratio of baffle and flowrate, fin spacing and flowrate, fin height and flowrate, fin spacing and height ratio of baffle, should be carefully considered when optimizing maximum temperature;the interactive impact of outlet area and flowrate should be considered when optimizing pressure loss. The weight could largely affect the optimal result and optimal value, which should be carefully designed for application. The differences in maximum temperature and pressure loss could achieve as high as 19.8 degrees C and 18.6 Pa when the weight of maximum temperature varied from 0.1 to 10.
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