The coupling effect among multiple structural parameters is a key factor restricting the analysis and optimization of the Pneumatic noise mechanism in Spring-Loaded Pressure Relief Valves. To address this, a comprehensive numerical study was conducted, focusing on both pneumatic noise modeling and surrogate-based optimization of the valve. First, a numerical model for pneumatic noise prediction was developed and validated using a specially designed test bench. The results showed that the deviations between simulation and experiments were less than 3% in mass flow rate and below 5% in sound pressure level, demonstrating the high accuracy of the computational aeroacoustics approach adopted. Subsequently, to enhance noise reduction performance, the outer diameter of the valve disc, the nozzle diameter, and the edge depth of the anti-impact disc were selected as optimization variables, and a Radial Basis Function (RBF) model was employed as an efficient surrogate predictor. Finally, a grey wolf optimizer algorithm was applied to optimize these parameters based on the RBF model. The optimized design was further verified via simulation. The results indicated that the proposed method achieved a reduction of 3.87 dB in SPL at selected receiver, offering valuable insights for related engineering applications.

Excessive computational cost is one of the main reasons limiting the application of regression analysis in engineering data mining. In this article, an undersampling method is proposed to screen the important samples from the initial dataset, aiming to save the computation cost by reducing the size of the training samples. A cost function is constructed by the weighted sum of the sample distance in the input space and the output change around each sample, and the weights of the above two terms are derived by the Lagrange multiplier method. A modified simulated annealing algorithm is designed to minimize the cost function to obtain the optimal undersampling results. The experiments on mathematical functions and engineering cases show that the proposed method outperforms the other methods in terms of regression accuracy and can save a great computational cost in regression analysis. The proposed undersampling method is applied to the data modeling of a shield machine, in which the Earth pressure is predicted by four operation parameters. The results indicate that the proposed method performs best in most experiments. The undersampling of training samples greatly reduces the computation time by 99.10%, but the prediction accuracy is only reduced by less than 10.00%. Considering the limited computing resources of engineering equipment and the need for rapid response, the proposed undersampling method provides a solution for the engineering application of regression analysis.

High-intensity turbulence and interactions among multiple structural parameters are the main factors hindering effective noise prediction in spring-loaded pressure relief valve (SLPRV). On this basis, an in-depth numerical modeling and mechanism analysis are carried out in this paper, where the Computational Fluid Dynamics (CFD) method and Computational Air Acoustic (CAA) are implemented to analysis the noise characteristics of the transient flow field when gas flows through the valve,on this basis, the attenuation characteristics and directivity of the noise were analyzed. In addition, a noise experimental platform for the SLPRV and an acoustic acquisition matrix were set up to verify the accuracy of the simulation results. In order to accurately predict its noise directivity, three key parameters at the valve orifice were selected for research, and a Kriging high-fidelity surrogate model (KHFSM) of the valve structure and noise directivity was constructed. The results show that, under different key structures of the valve, the determination coefficient R2 of the noise predicted by KHFSM reaches 0.969, and the maximum error is less than 1 dB, it can accurately predict the noise directivity of SLPRV, and establish a theoretical and methodological framework underpinning the acoustic optimization of safety valves in nuclear power systems.

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 oscillating hydrofoil, a device used for collecting environmentally friendly tidal energy, is the focus of the study. The flexibility of the hydrofoil's trailing edge can impact its surface pressure distribution, lift, and moment characteristics. To improve the energy harvesting performance of oscillating hydrofoils, it is important to conduct thorough research on their energy harvesting mechanism. Therefore, numerical analysis is employed to develop a numerical model of the fully passive oscillating hydrofoil with the flexible trailing edge. The dynamic development behavior of surface vortices on hydrofoils is analyzed, demonstrating that the fluid–structure interaction between the hydrofoil and the surrounding fluid alters the hydrofoil's motion. The vortex patterns and pressure distribution on the hydrofoil surface are also affected, ultimately influencing the energy harvesting performance. By optimizing the flexibility coefficient of the fully passive oscillating hydrofoil with a flexible trailing edge, the energy harvesting performance of the oscillating hydrofoil is improved. When the maximum chord offset δm= 0.1c and the flexibility coefficient n= 2, the energy harvesting efficiency is 31.37%, and the average power coefficient is 1.17. Therefore, increasing the tail flexibility can be considered to enhance energy harvesting performance when designing the fully passive oscillating hydrofoil. The research provides a comprehensive analysis of energy harvesting performance, addressing the dynamic problem of the fully passive oscillating hydrofoils with flexible trailing edges. The findings of this study may provide guidance for the design and optimization of tidal energy harvesting devices with similar structures.

In this work, a multi-output model is proposed based on extreme learning machine, which uses the regression information of training samples and the correlation of outputs to predict multiple responses for the new point. Two kernel matrices are designed to evaluate the regression relationship of training samples and the correlation of the outputs, respectively. A five-fold cross-validation method is used to assess the training error, and a heuristic algorithm is used to optimize the hyperparameters of the proposed model. The results of mathematical problems indicate that the performance of the proposed model is better with more training samples and is positively correlated with the correlation of outputs. The proposed model produces more competitive performance in terms of prediction accuracy and computational cost than the benchmark multi-output models. The proposed multi-output model is applied to the multi-objective optimization of a dental implant, showing its application potential in engineering design and optimization.

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.

Main steam relief isolation valve (MSRIV) is one type of advanced control valve used in pressurized water reactors (PWR), such as Hualong One (HPR 1000 in China) and European Pressurized Reactor (EPR). It can provide overpressure protection for the steam generator (SG) and used for waste heat discharging. However, MSRIV may show undesirable operations such as reclose slow under high pressure & temperature conditions (e.g., 8.9 MPa & 316 °C) and/or cannot be opened timely under low pressure & temperature conditions (e.g., 0.14 MPa & 316 °C). To explore the root causes of these undesirables, an experimental based analysis was specially carried out in this paper. For valve performance measurement, an experimental test rig was developed, thereby not only dynamic responses of the MSRIV under different operating conditions, but also the influence of valve key parameters can be analyzed. The results show that both the system pressure and the throttle area have great influence on the dynamics of the MSRIV. Upon the same disk throttle area, the valve opening time decrease with the inlet pressure, while the valve reclosing time increase with it. For the same inlet pressures, larger throttle area usually led to longer valve opening time and the shorter valve closing time. The results obtained in this study not only provide a reference for the valve early design stage, but also can be used as a criterion for the subsequent numerical modeling works which is of great importance for the flow details acquisition and valve mechanism exploration.

In this article, a robust ensemble model is proposed based on extended adaptive hybrid functions and fuzzy clustering. In the outlier detection stage, each sample is assigned memberships to judge whether it is an outlier or not, where the memberships are determined based on the responses of the ensemble surrogate model of each cluster. Then, the detected outliers are removed from the initial training samples, and the final prediction model is constructed based on the remaining normal samples. The results of numerical problems and the in-situ dataset from a combine harvester show that the proposed model can provide accurate detection results for outliers and accurate prediction results for new points. The sensitivity analysis based on the proposed robust ensemble model indicates that the angle of guide plate, the open rate of cleaning fan, and the height of header have a greater effect on the cleaning loss of combine harvester.

Pressure safety valves (PSVs) maintain the safety and stability of the pressure systems used in nuclear power plants. However, it may show dynamic instabilities under extreme conditions, such as flutter or low-frequency cycling. To explore the underlying mechanism of these undesirable behaviors, an in-depth analysis on the fluid force and flux is essential. As a complex nonlinear system, the fluid disk force and flux are affected by many factors, such as pressure, temperatures and so on. To overcome this, a surrogate model-based method which can account for all of these factors were adopted to establish the relationship between valve design parameters and steady state characteristics. Among the used surrogate modeling methods, the RBF was identified as the optimal model. With the RBF model, Sobol’s method was used to identify key parameters for the fluid disk force and flux, based on which, the effect of the identified key parameters on the valve static performance were analyzed. The method proposed in this paper can not only used to valve behavior predictions, but also available for the mechanism exploration of dynamic instabilities.

Cavitation frequently arises in the safety valve of nuclear power plants’ secondary circuits operating under high pressure conditions. This study integrates valve flow characteristics and velocity strain rate corrections into the Zwart-Gerber-Belamri model to accurately simulate cavitation inside the valve, reducing the impact of physical empirical coefficient variations on cavitation length prediction. Subsequently, a visualisation test rig is developed to validate the accuracy of the numerical model, and experimental cavitation results are obtained using the grayscale detection method. The evaporation/condensation coefficients are optimised using the AES-MSI model and GA based on the experimental results. The accuracy of the constructed model is validated by comparing it with experimental results obtained under various operating conditions. Finally, the high-fidelity numerical model is employed to investigate the effects of pressure drop and valve openings on cavitation, elucidating the underlying mechanisms governing cavitation variations resulting from pressure drops. Furthermore, a comprehensive equation is derived to determine the effective flow area, aiding in the identification of cavitation locations and offering insights into the relationship between cavitation behaviour and valve openings. The modified cavitation model proposed in this study can be readily extended to investigate cavitation prediction in other valves or throttle elements.

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.

针对弹簧式安全阀快速回座使得阀盘与喷嘴之间发生冲击进而导致密封结构破坏的问题,提出了考虑流-固-热多场耦合的阀门冲击过程分析方法.建立了用于安全阀瞬态过程仿真的CFD模型,同时针对弹簧式安全阀的固体结构建立了有限元模型,并开展了热力学、静力学与动力学仿真和试验对比.结果表明,仿真和试验所测得的阀瓣位移的变化趋势一致,两者偏差为9.86%,最后时刻的阀瓣速度偏差为3.70%,冲击力两者的偏差为6.94%;随着介质压力增大到18.50 MPa,阀盘最大应力和最大接触压力都呈现了先减少、再增大的趋势,同时,密封面实际接触位置由外圈过渡到内圈.研究结果可为安全阀前期设计提供依据.

O-ring seal is widely used in pressure system to prevent leakage of the fluid medium. However, in actual situations, excessive pressure of the system and the unreasonable structure usually led to the severe distortion of the O-ring seal, which may result in the seal failure and thus medium leakage. To explore the mechanism of these failures, a two-way fluid-structure interaction (FSI) based analysis was carried out in this paper, where the model of O-ring seal is constructed based on the Mooney-Rivlin hyperplastic constitutive model and the dynamic mesh technique. Through simulations, the stress distribution and the deformation of the O-ring seal under 100 MPa pressure conditions were predicted, verifying the ability of the developed numerical model. On this basis, both one-way and two-way FSI simulations were performed, and the pressure ratio of the sealing surface were compared, which indicates that there is obvious difference between these two types of simulations, the pressure ratio produced by the one-way simulations were higher than that obtained from two-way FSI simulations. Since the two-way FSI takes the sealing surface changing into consideration, to ensure the accuracy, it was used in the follow-up analysis works. By observing the simulated strain and stress distribution, it is determined that seal surface near the two right angles of the limiting plate is the key part prone to medium leakage, which provides a basis for the structural optimization design of the O-ring seal.

Pressure relief valve (PRV) is one of the important control valves used in nuclear power plants, and its sealing performance is crucial to ensure the safety and function of the entire pressure system. For the sealing performance improving purpose, an explicit function that accounts for all design parameters and can accurately describe the relationship between the multi-design parameters and the seal performance is essential, which is also the challenge of the valve seal design and/or optimization work. On this basis, a surrogate model-based design optimization is carried out in this paper. To obtain the basic data required by the surrogate model, both the Finite Element Model (FEM) and the Computational Fluid Dynamics (CFD) based numerical models were successively established, and thereby both the contact stresses of valve static sealing and dynamic impact (between valve disk and nozzle) could be predicted. With these basic data, the polynomial chaos expansion (PCE) surrogate model which can not only be used for inputs-outputs relationship construction, but also produce the sensitivity of different design parameters were developed. Based on the PCE surrogate model, a new design scheme was obtained after optimization, in which the valve sealing stress is increased by 24.42% while keeping the maximum impact stress lower than 90% of the material allowable stress. The result confirms the ability and feasibility of the method proposed in this paper, and should also be suitable for performance design optimizations of control valves with similar structures.

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.

针对能源工业领域中常见的压力容器-管路-安全阀系统(以下简称:压力系统)开展了系统级变保真度模型的研究.从提高模型的仿真效率同时保证计算精度的角度出发,分别基于等效压力点方法、特征线理论和CFD技术对压力系统中的压力容器、连接管路和安全阀进行了元件级模型的构建和系统级模型的耦合.为了验证提出的建模方法的准确性,结合已有的实验结果进行了模型的精度验证,结果表明,构建的系统级变保真度模型具有较好的压力系统动态特性仿真能力,能够对压力容器和管路内的压力波动以及弹簧式安全阀的动态响应进行准确的计算.

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.