Multi-fidelity surrogate (MFS) models have garnered significant attention in the field of engineering optimization due to their ability to attain the desired accuracy at a reduced cost. However, most previous MFS models employed a single scaling factor for the global design space, which posed challenges in adaptively adjusting the scaling factor within local design spaces. To address this issue, this paper proposes a novel MFS model based on design variable correlations (MFS-DVC). MFS-DVC introduces local characteristics to the scaling factors by leveraging correlations between design variables, enabling adaptive adjustments of the scaling factors at different positions within the design space. Moreover, MFS-DVC offers a more comprehensive exploration of relationships and information among high-fidelity (HF) data points by utilizing the correlations between design variables. This enhancement contributes to improving the accuracy and robustness of the model. The performance of MFS-DVC was evaluated by comparing it with three benchmark MFS models and two single-fidelity surrogate models on 22 test functions and one engineering problem involving a hydraulic press. Additionally, the cost ratio and combination of HF and low-fidelity samples were studied to assess their impact on the performance of the MFS-DVC model. The results demonstrate that MFS-DVC consistently delivers competitive performance in terms of prediction accuracy and robustness.

Surrogate models serve as powerful tools for engineering application but constructing highly accurate models with limited samples remains a challenge. Inspired by this, this paper proposes a general random projection enhancement method (RPE) aimed at improving surrogate model performance. Taking least squares support vector regression (LSSVR) as an example, we conducted numerical experiments to evaluate the feasibility of the RPE method. The RPE-enhanced LSSVR demonstrates superior predictive accuracy and robustness compared to the unenhanced model and other benchmark models. The results show that the RPE method can not only enhance the predictive accuracy of the model but also improve the robustness of the model. Meanwhile, analysis of optimization experiments indicated that the enhanced LSSVR model obtained better solutions compared to the unenhanced LSSVR model. Even when extended to high-dimensional problems, the RPE method remains effective. Furthermore, the applicability of RPE method extends from the LSSVR model to other models, as demonstrated by the enhancement of the extreme learning machine, regression kriging, and radial basis function neural network. More importantly, the feasibility of the RPE method is also verified in engineering problems, obtaining the same results. This method offers a reliable alternative for real-world engineering optimization problems.

Bi-fidelity surrogate (BFS) modeling is a powerful technique to mitigate the constraints of computational time and resources in high-fidelity (HF) models. However, obtaining sufficient HF samples in real-world scenarios remains challenging. This issue drives the creation of dependable surrogates capable of performing effectively on limited datasets. In this research, an innovative method is devised for constructing a BFS model based on scaled correlation construction and penalty minimization, with the goal of improving the performance of surrogate models when data are limited. This method starts by establishing a tuning factor that measures the contribution of different features to the response, thereby reducing the impact of redundant features on model construction. This factor is integrated into the construction of the discrepancy function to represent the variations between low-fidelity (LF) and HF samples. Additionally, a regularization constraint is imposed on the model parameter to prevent overfitting, which in turn increases the model’s robustness and interpretability. To validate the superiority of the developed BFS model, five leading surrogate models are selected for comparison. Experiments are conducted across various dimensions and nonlinearities of numerical problems, showcasing the competitiveness of the developed BFS model. Furthermore, a case study in engineering illustrates the practical application of the developed BFS model in the real-world scenarios.

The globe valve fails to open completely sometimes due to insufficient lifting of the valve disc during the operation of the nuclear power plant. The main reason is the inadequate understanding of changes in fluid force. Therefore, conducting research on the fluid force of the globe valve is crucial. In this study, we establish a high-fidelity computational fluid dynamics (CFD) numerical model to investigate the flow characteristics of a novel balanced globe valve. Based on the analysis results, we concluded that the size of the throttle orifice is an essential factor affecting fluid force. Therefore, the impact of the throttle orifice sizes on the fluid force is studied. Meanwhile, the coupling influence of different valve disc displacements and inlet pressures on fluid force is quantitatively analyzed. This study provides a basis for the design of globe valves and has potential value for the dynamic control and energy utilization.

The roller pump is a distinctive device that delivers fluid in an analogous manner to the peristaltic movement of a biological organism. The pump with the unique mechanism can isolate the liquid from the pump's mechanical components effectively, thereby preventing contamination of the fluid. Therefore, the pump is widely used in extracorporeal circulation machines as artificial hearts for extracorporeal circulation of blood. However, the roller pump exhibits flow pulsation, and in severe cases, that may potentially harm the human body during extracorporeal circulation surgeries. It is necessary to optimize the structure of the roller pump, reduce the degree of flow pulsation of the roller pump and improve the smoothness of the flow transmission. In order to minimize the extent of flow pulsation, the high fidelity fluid–solid interaction (FSI) model of roller pump is established, the accuracy of the model is verified by experiment. Two methods of optimizing the pump structure are proposed. First, the optimization method based on the flow compensation, a new structure model of Y- shaped is established, that reduce the degree of pulsation by superimposing the flow curves with phase difference for flow compensation. Another one is the rapid and efficient optimization method based on the surrogate model, that the surrogate model combines with the optimization algorithm to optimize the structural parameters. The surrogate model is used to replace the FSI model, and the optimal configuration of the internal structural variables is obtained through the Multi-island genetic algorithm (MIGA). And the accuracy of the surrogate model verified by FSI analysis. The results indicate that the two optimization methods have their own advantages in reducing the pump flow pulsation and the pump flow pulsation performance has been improved significantly.

The peristaltic pump is a high precision biomimetic pump widely used in the medical industry. However, the pump has flow pulsation, due to its special structure. The serious flow pulsation can dramatically reduce delivery accuracy and stability. Meanwhile, the service life of the peristaltic pump is affected by the conveying capacity. The flow pulsation degree and transportation capacity of the pump are affected by the structural parameters of its key components. An in-depth study of peristaltic pumps is necessary in order to reduce the degree of pulsation and increase service life. The paper skillfully combines the fluid-solid interaction (FSI) mechanism model with the surrogate model to deeply study the influence of key structural parameters of the peristaltic pump on its performance, and the multi-objective optimization design of the pump is carried out by using the multi-island genetic algorithm (MIGA). The results show that for the average flow, the hose inner diameter has the greatest influence, followed by the working circle diameter. The roller diameter has very little effect. The hose inner diameter and working circle diameter have a minor coupling effect on the roller diameter. It slightly affects the trend of the average flow rate with the roller diameter. The hose inner diameter also has a slight coupling effect on the working circle diameter. For the instantaneous flow rate percentage, the effects of all three key parameters are significant. The hose inner diameter has an obvious coupling effect on the working circle diameter and the roller diameter, which affects the trend of the instantaneous flow rate percentage with the working circle diameter and the roller diameter. The optimized pump has a 43.3% decrease in the percentage of flow rate and a 59.6% increase in the average flow rate, indicating significant improvements in transport stability and capacity.

Nuclear safety valve is a critical piece of equipment in a nuclear power plant, which is used to prevent irreversible damage caused by a sudden increase in pressure. However, there are some instances wherein valves may fail to function properly, which can have significantly impact the safety of the entire pressure/energy system. The main causes behind this phenomenon is the effect of fluid-structure coupling between the fluid force and valve disc. To better understand the fluid force, a high-fidelity computational fluid dynamics (CFD) model is established to predict the behavior of fluid forces and the location of vortices in the valve. Moreover, a visual fluid force test rig is used to verify the accuracy of the CFD model. Based on the validated CFD model, the mechanism of fluid force differences for two typical valve discs are analyzed in detail, together with the univariate effects of groove depth and valve opening on the fluid force. Based on the univariate analysis results, the coupling effect of groove depth and valve opening on fluid force is quantified using the supervised learning algorithm and Sobol sensitivity analysis. The study provides a new perspective on the characteristics of valve fluid force, and highlights the significant potential of dynamic control and energy conservation of valves.

The flow systems in nuclear power plants and aircraft engine fuel pipelines are often subjected to ex-treme high-pressure conditions, which can induce cavitation and severely affect essential system com-ponents. Orifice plates are the most typical infrastructures representing the flow principle of the aboved flow systems. In this paper, a modified cavitation model combining local flow characteristics, supervised learning, and genetic algorithms is proposed to investigate the cavitation flow characteristics of orifice plates under high-pressure conditions. The modified cavitation model eliminates the influence of the bub-ble diameter and nucleation site volume fraction on mass flow rate and achieves dimensionality reduc-tion at the physical level. The relationship between evaporation/condensation coefficients and mass flow rate is constructed by supervised learning, and the two coefficients are determined using the genetic al-gorithm. The mass flow rates are calculated by the modified cavitation model within a 5% experiment error, proving the accuracy of the modified cavitation model. The effect of the diameter ratio (the di-ameter of the pipe to the orifice plate) and pressure drop on the mass flow rate are obtained based on the validated cavitation model. Finally, an empirical formula for calculating the mass flow rate based on the diameter ratio and pressure drop is derived. The modified cavitation model shows great potential for cavitation prediction applications for throttling devices such as nuclear power safety valves and aircraft engine nozzles.(c) 2022 Elsevier Ltd. All rights reserved.

Peristaltic pump is widely used in pharmaceutical, medical and other industries because of its advantages of no pollution and high transmission accuracy. The fluid medium is conveyed by the rollers alternately squeezing and relaxing the hose, so when the rollers away from the hose as the rebound of the hose will cause the backflow of liquid, thus forming the flow pulsation. The more serious the flow pulsation, the more difficult it is to control the output flow, the peristaltic pump flow pulsation reduction is of great importance for the improvement of its transmission accuracy. For exploration and optimization of mechanism design, a response surface methodology (RSM) model based mechanism optimization method is proposed. Two peristaltic pump parameters were chosen as the primary design variables for the surrogate model, with the objective of minimizing the instantaneous flow ratio of the peristaltic pump flow curve. With the two design variables, Optimal Latin hypercube sampling (OLHS) based design of experiments (DoE) were carried out, with which different Fluid-Structure-Interaction (FSI) numerical model were developed to calculate the instantaneous flow rate ratio the Peristaltic pump flow curve, thereby the RSM model was constructed to establish the relationship between the design variables and the flow pulsation performance. Based on the RSM model, Peristaltic pump design optimization was performed with the help of genetic algorithm (GA). Finally, a peristaltic pump design scheme that may effectively lessen the flow pulsation was obtained. To verify the results, another simulation was performed and compared the results with those generated by the optimization, achieving good agreement and demonstrating the feasibility of an optimal design approach.

Peristaltic flow is a common phenomenon in various natural physiological processes, such as the flow of blood and urine. Peristaltic pumps, which are a typical example of such transfer mechanisms, have extensive applications in the pharmaceutical, petrochemical, and biomedical industries. Nevertheless, the peristaltic pump inevitably produces flow pulsations during its operation, which can severely impede the precise transmission of fluid media. Consequently, comprehending the pulsation mechanism and investigating the impact of critical parameters on pulsation are immensely important for achieving optimal design of peristaltic pumps. In this paper, a high-precision three-dimensions (3-D) Two-way Fluid-structure Interaction (TFSI) model is developed, which takes into account both the hyper-elastic properties of the flexible hose and the deformation of the fluid domain. The mass flow rate of the peristaltic pump for one cycle is determined by combining the solid pre-compression technique with the dynamic mesh technique, and then a detailed explanation of the pulsation mechanism is provided, combining the state of the flexible tube and the flow characteristics within the tube at every moment. To verify the TFSI model, the experimental tests are carried out and the results obtained from the simulation are compared with the experimental results, which indicates the accuracy of the model. Based on the validated TFSI model, the effects of critical parameters on pulsation are analyzed, including plugging rate, roller speed, number of rollers, and the diameter of rollers. These findings suggest that the TFSI model is an effective tool for analyzing and optimizing the design and performance of peristaltic pumps.

A pressurized vessel-pipe-safety valve (PVPSV) combination is a commonly used configuration in nuclear power plants, and a good numerical model is essential for the system design, sizing and performance optimization. However, owing to the large-scale and cross-scale features, it is still a challenge to build a system level numerical model with both high accuracy and efficiency. To overcome this, a novel system level modeling method which can synthesize the advantages of various models is proposed in this paper. For system modeling, the analytical approach, the method of characteristics (MOC) and the surrogate model approach are respectively adopted to predict the dynamics of the pressure vessel, the connecting pipe and the safety valve, and different models are connected through data interfaces. With this system model, dynamic simulations were carried out and both the stable and the unstable system responses were obtained. For the model verification purpose, the simulation results were compared with those obtained from experiments and full CFD simulations. A good agreement and a better efficiency were obtained, verifying the ability of the model and the feasibility of the modeling method proposed in this paper.

Safety valves as the last barrier of the pressure vessel and piping system ensure the stability of the whole system. However, there are specific situations where valves may not operate properly, which can have a significant impact on the safety of the entire system. The reason for this is a lack of understanding of the dynamic characteristics of valves, of which fluid forces are the most critical factor. In this paper, a high-precision test rig was built to test steady-state fluid forces of a direct-acting relief valve, where proportional-integral-derivative control (PID) control was applied in order to obtain more accurate multi-stage flow rates adjustments. In addition, a adjustment mechanism has been designed to obtain a more accurate valve opening. Based on this test rig, the steady-state fluid force at different openings and different flow rates are conducted, the relationship between fluid forces, flow rates and pressure drop of the valve is analyzed from the test data, which provide an in-depth understanding of the dynamic characteristics of the valves.

Main steam safety valves are commonly used in nuclear power plants to provide final protections from overpressure events. Blowdown and dynamic stability are two critical characteristics of safety valves. However, due to the parameter sensitivity and multi-parameter features of safety valves, using traditional method to design and/or optimize them is generally difficult and/or inefficient. To overcome these problems, a surrogate model-based valve design optimization is carried out in this study, of particular interest are methods of valve surrogate modeling, valve parameters global sensitivity analysis and valve performance optimization. To construct the surrogate model, Design of Experiments (DoE) and Computational Fluid Dynamics (CFD) simulations of the safety valve were performed successively, thereby an ensemble surrogate model (E-AHF) was built for valve blowdown and stability predictions. With the developed E-AHF model, global sensitivity analysis (GSA) on the valve parameters was performed, thereby five primary parameters that affect valve performance were identified. Finally, the k-sigma method is used to conduct the robust optimization on the valve. After optimization, the valve remains stable, the minimum blowdown of the safety valve is reduced greatly from 13.30% to 2.70%, and the corresponding variance is reduced from 1.04 to 0.65 as well, confirming the feasibility and effectiveness of the optimization method proposed in this paper.