Structural Parameters Research Articles

Heat transfer and fluid flow analysis in circular tubes with multi-delta-winglets vortex generators

In this work, the positive projection plane of self-join winglet vortex generator is modified into a non-circular design, aiming to provide novel insights into optimizing vortex interaction in tubes equipped with winglets. The experimental and numerical investigation of heat transfer and fluid flows in circular tubes equipped with multi-delta-winglets vortex generators is conducted. The influence of three structural parameters (included angle (α), blockage ratio (BR), winglet pairs number (PN)) on the thermal performance of the enhanced tube are explored across a Reynolds number range of 7577 to 27,276. It is found that the vortex pairs near the tube wall contribute to enhancing the mixing uniformity of fluid flows in tubes. Furthermore, the dissipation intensity of the vortex pairs near the tube wall is increased with increasing α. The Nusselt number and friction factor are increased by 1.90–2.32 and 2.23–5.10 times, respectively, compared with the smooth tube. The maximum thermal enhancement factor reaches 1.63 when BR = 0.10, PN = 4, and α = 90°. Optimization strategies are performed through similarity analysis, and the similarity equations between structural parameters and flow field characteristics are obtained. These findings offer valuable theoretical insights for the design of winglet structures and the optimization of flow fields.

Design of a high-precision linear regulating ball valve

In this paper, a high-precision linear regulating ball valve is designed from four aspects: positioning accuracy, sealing performance, flow area and flow coefficient. A mathematical expression for the relationship between spool thickness, incision angle and flow coefficient was developed. The correlation matrix P between the structural parameters and the flow coefficient is obtained by surface fitting. The structural parameters under linear requirements are further determined by determining the P matrix. A more accurate linearization of the flow characteristics was achieved.The CFD data showed that the error of the optimized flow rate with respect to the ideal linear data was less than 0.15 kg/s. The average error was reduced by 43.8% when compared with the minimum error of different structures. The variance decreased by about 58.2% compared to the minimum variance for different structures. The final flow test of the optimized ball valve was carried out. The error between the flow test data and the CFD data is less than 0.14 kg/s and the maximum relative error is about 6.58%.

A study on influence of sintering temperature, diameter and volume fraction of metal hollow spheres on the mechanical properties of metal hollow spheres composites

The structural parameters (the sintering temperature, diameter, and volume fraction of the hollow spheres) have a considerable influence on the mechanical properties of metal hollow sphere composites (MHSCs). Hence, the influence laws of the structural parameters on the mechanical properties of MHSCs possess certain research significance. To obtain these laws, MHSCs with different structural parameters were fabricated using a pressure casting method. Subsequently, the density of the MHSCs was determined through the direct measurement method. Meanwhile, X-ray diffraction and scanning electron microscopy were employed to conduct in-depth analyses of the phase composition and microstructure of the MHSCs. Additionally, the compression performance of the MHSCs was measured with the assistance of a universal mechanical testing machine. The analysis of the microstructure and phase composition results revealed that MHSCs consisted of γ-Fe, Al, Si, Fe₂Al₇Si, Fe₂Al₄Si, and Al₈Si₆Mg₃Fe phases, with the transition layer composed of the Fe₂Al₇Si phase. The results of the compression performance indicated that the compression performance of MHSCs increased with the rise of the sintering temperature and decreased with the increase of the volume fraction. However, as the diameter of MHS increased, the yield strength of MHSCs declined while the deformation strength of MHSCs increased. When the diameter of the MHSs was 2.55mm and the volume fraction was 40%, the compression performance of the MHSCs reached its optimum. At that point, the yield strength was 113.65±3.37 Mpa, and the average deformation strength was 81.26±5.45 Mpa.

Structural parameters optimization of microwave radiation antenna in hydrate reservoir based on multiphysical coupling model

Microwave heating has emerged as a promising technology in hydrate mining, attracting significant interest. This study focus on optimizing the structural parameters of microwave radiation antenna via a multiphysical coupling model. Subsequently the model is validated through experimental results. The microwave radiation simulation model is then developed to evaluate the antenna radiation performance and to elucidate the temperature distribution mechanism within the reservoir. The optimized structure features rectangle slots with an angle of 75° and a length of 28 mm. When this optimized antenna is deployed in a one-meter radius reservoir and heated for 10 hours, it rises the average temperature from 2 °C to 7.11 °C. Moreover, the design improves the thermal uniformity within the gas hydrate reservoir, achieving a temperature standard deviation of 7.76 °C. Post-heating uniformity indicates effective microwave distribution. Overall, these results affirm that microwave heating, particularly when utilizing an optimized antenna, effectively enhances the reservoir’s sensible heat and aids in hydrate decomposition.

Visual acuity in the context of retinal neuroaxonal loss in people with epilepsy.

Recent studies reported a significant retinal neuroaxonal loss in people with epilepsy (PWE). However, the impact of these structural alterations on visual function, i.e., visual acuity is yet unknown. In this prospective cohort study, 70 PWE and 76 healthy controls (HC), all aged 18-55 years, underwent an assessment of visual acuity with 100 % high contrast (HCVA) and 2.5 % low contrast (LCVA) Sloan letter charts. Thickness of the global peripapillary retinal nerve fiber layer (G-pRNFL) and volume of the ganglion cell inner plexiform layer (GCIP) were assessed with spectral-domain optical coherence tomography (OCT). For the statistical analyses, the epilepsy group was subdivided into PWE with sodium channel blocking (SCB)-drug intake (n = 52) and PWE without SCB-drug intake (n = 18), since an effect of SCB-drugs on visual perception has been reported previously. The overall PWE cohort presented significantly lower structural retinal measures, i.e., G-pRNFL thickness (97.57 ± 9.06 µm) and GCIP volume (1.99 ± 0.13 mm3) than HC (101.31 ± 8.28 µm, p = .01; 2.10 ± 0.15 mm3, p < .001). Subgroup analyses revealed that PWE who were treated with SCB-drugs had a significantly reduced G-pRNFL thickness (96.61 ± 9.70 µm, p = .01) and GCIP volume (1.98 ± 0.14mm3, p < .001) compared to HC, while PWE without SCB-drugs (100.36 ± 6.32 µm, 2.01 ± 0.13 mm3) did not differ from HC or PWE with SCB-drugs. In visual acuity tests (HCVA and LCVA), the overall PWE cohort (52.28 ± 8.56; 31.71 ± 8.49) scored significantly lower than HC (56.57 ± 4.74, p = .001; 35.13 ± 5.50, p = .04). In subgroup analyses only PWE with SCB-drugs presented significantly lower HCVA (51.25 ± 9.35, p = .003) and LCVA (30.04 ± 8.93, p = .03) scores compared to HC, while visual acuity scores did not differ between PWE without SCB-drugs (55.25 ± 4.75, 36.53 ± 4.50) and HC. PWE with SCB-drugs had significantly lower LCVA scores than PWE without SCB-drugs (p = .03). Importantly, no association was found between visual acuity scores and structural parameters, neither in the overall sample, nor in any of the subgroups. Retinal neuroaxonal loss in PWE was not associated with reduced visual acuity under high and low contrast. Instead, our findings reinforce SCB-drug intake as an important factor for reduced visual acuity under high and low contrast.

Experimental study on the hydrodynamic behavior for a novel compound anti-vibration submerged floating tunnel system

Anchor cable-type submerged floating tunnel (SFT) is susceptible to large amplitude vibration responses under wave-current excitations. This becomes a critical issue to the safety, stationarity, and driving comfort of SFT. In this study, a novel compound anti-vibration SFT system is proposed, featuring a three-tube SFT reinforced with a rigid truss and an external floating energy dissipation device with polyvinyl chloride (PVC) porous media. The hydrodynamic behavior of the proposed compound SFT system is experimentally explored and compared with that of conventional SFTs utilizing a large wave-current flume. Sensitivity analysis for the key parameters of SFT is conducted using hydrodynamic parameters (such as wave height and wave period), structural type, and structural parameters (draft depths). The results show that the proposed SFT system has excellent anti-vibration performance when compared to conventional SFTs, reducing over 10 and 60 times in translational and rotational motion, respectively. The proposed SFT system with the floating tank at half draft depth shows the best anti-vibration performance under different wave-current excitations. The proposed SFT system achieves excellent anti-vibration performance without increasing the burden on cable tension, solving a common issue in conventional anti-vibration methods. The findings offer valuable insights into the anti-vibration design and application of SFT.

Analysis of characteristics of seawater desalination-solar chimney power plant under double-layer collector

To improve the output characteristics of seawater desalination-solar chimney power plants, enrich the theoretical research basis and promote its commercial application, this paper introduces the concept of multilayer flow channel, establishes four new types of multilayer collector flow channel, and constructs the corresponding heat and mass transfer mathematical models, revealing the mechanism of the influence of different flow channel and structural parameters on the system performance. The results showed that the surround-flow system extended the heat absorption time of the airflow, had two thermal storage layers, and had the best performance. The daily average power generation was 43.3 kW, and the average water production rate was 87.2 g/(h·m2), which was 132.8 % and 91.2 % higher than the traditional type’s, respectively, but had the longest start-up time. For the surround-flow system, the main reason for the flow loss increasing is swirling regions below the inlet and the transition section caused by the structure itself. Both the inlet width increasing and the roof’s inclination angle decreasing can increase power generation, with little impact on water production. Corner width increasing results in a significant decrease in water production, with little impact on power generation. The determination of structural parameters should also consider operational costs.

Silicon carbide PIN diode for tritium detection

We have designed a prototype of silicon carbide PIN diode for tritium detection. Monte-Carlo calculations showed the necessity to work in vacuum and employ thin and low-density coating layers, and drew the energy deposition profile in the device. The deposited energies have then been used to implement finite-elements simulations, in order to optimize three structural parameters (n− doping concentration, p doping concentration, p zone thickness) maximizing the electrical response. Temperature lowering improves the diode electrical response.

Studies on the free vibrations of tether of submerged floating tunnel involving tube motion by using different mechanical models

To accurately acquire the frequency/mode of the tethers of the tether-type submerged floating tunnel (SFT), which is an innovative underwater traffic structure with great potential, the dynamic frequency (DF) and wet mode (WM) of the tether involving the tube motion are studied. The novelty lies on the proposed novel method where the analytical relation of the tether’s DF and WM with the moving boundaries and the structural parameters is directly built. The gap that the unreported premise of quasi-static treatment for the global vibratory SFT and the unmentioned principle of the top tension of the tether equaling the net buoyancy within its supporting range is filled. Firstly, four mechanical models named M1∼M4 are respectively established by equating the tether as a beam or string with moving boundaries, taking into accounts the bending stiffness, wet-weight and other geometrical/material factors. Secondly, the relationships between the band-width, upper/lower limits of the DF and the inclination angle, vertical height, buoyancy-weight-ratio (BWR), motion amplitude are founded. Thirdly, the distribution laws of the DF during the vertical periodic motion of tube, as well as the parameter sensitivity range of the WM to the span-wise effect of wet-weight and the vertical height, are studied. The results show that the tether exhibits DF under the tube motion. The band-width of DF is sensitive to the BWR, inclination angle, vertical height and motion amplitude, and its upper/lower limits exhibit varying degrees of variation patterns with the changing of these parameters. The wet-weight and the vertical height are two main influencing factors of the WM. The proposed method provides a new treatment idea for further in-depth research on the locking zone of the vortex-induced vibration (VIV) and parametric vibration for the tether of SFT.

Deep expert network: A unified method toward knowledge-informed fault diagnosis via fully interpretable neuro-symbolic AI

In recent years, intelligent fault diagnosis (IFD) based on Artificial Intelligence (AI) has gained significant attention and achieved remarkable breakthroughs. However, the black-box property of AI-enabled IFD may render it non-interpretable, which is essential for safety-critical industrial assets. In this paper, we propose a fully interpretable IFD approach that incorporates expert knowledge using neuro-symbolic AI. The proposed approach, named Deep Expert Network, defines neuro-symbolic node, including signal processing operators, statistical operators, and logical operators to establish a clear semantic space for the network. All operators are connected with trainable weights that decide the connections. End-to-end and gradient-based learning are utilized to optimize both the model structure weights and parameters to fit the fault signal and obtain a fully interpretable decision route. The transparency of model, generalization ability toward unseen working conditions, and robustness to noise attack are demonstrated through case study of rotating machinery, paving the way for future industrial applications.

Probabilistic seismic performance assessment of aqueduct structures considering different sources of uncertainty

This investigation aims to propose the compound intensity measure and establish the seismic performance assessment framework considering multiple uncertainties for aqueduct structures. For this purpose, taking the typical three-span aqueduct structure as an engineering case, the uncertainty sample pairs are generated by Latin Hypercube Sampling. The seismic response under different ground motion intensities is obtained by incremental dynamic analysis (IDA), and the mapping relationship between structural damage characteristics and limit states is clarified. According to statistical analysis, quantitative recommendation values for the record-to-record, design parameters, performance index values, limit states and finite element model fidelity uncertainties are determined. Then, the multivariate compound intensity measure IMC is developed by taking into account the correlation between single intensity measure. Furthermore, the seismic performance assessment framework of aqueduct structures is established by the single and compound intensity measures. The total dispersion by multiple uncertainty factors is determined by the square-root-sum-of-squares (SRSS) method and the effects of uncertainty factors on the seismic fragility analysis results of aqueduct structures is explored. Subsequently, based on the seismic fragility function and the seismic hazard function, the seismic risk assessment method considering multiple uncertainties is proposed. The analysis results show that with the increase of uncertainty factors, the seismic fragility curves of the aqueduct structure under different limit states rotate clockwise along the IMR. The 50-year exceedance probabilities considering multiple uncertainties is 2.57∼3.14 (PGV) and 1.92∼2.28 (IMC) times that of considering only record-to-record and structural design parameters, further revealing the traditional method can overestimate the potential seismic risk of aqueduct structures. On the other hand, compared with the IMC, the effect of uncertainty factors on the 50-year exceedance probabilities obtained by PGV is more significant. In a word, the compound intensity measure and the multiple uncertainty analysis method proposed in this investigation can be utilized for the seismic performance assessment system of aqueduct structures.

Time-Domain Load Identification and Dynamic Displacement Inversion of Discharge Sluice in Large Hydropower Stations Based on the Conjugate Gradient Least Squares Iteration Algorithm

The identification of flow-induced vibration loads is the key to solving the vibration problem of hydraulic structures induced by high-speed water flow. In this study, a time-domain load identification method for discharge sluices in large hydropower stations based on the conjugate gradient least squares (CGLS) iteration algorithm is proposed. The main idea of this method is to first construct a mathematical model through the response signal and the structural modal parameters for solving the equivalent load acting on the sluice structure. Then, it uses the iterative regularization method to solve the ill-posed problem in the load identification process of the traditional time-domain method. Numerical simulations show that, in comparison to the traditional Tikhonov regularization algorithm, the CGLS algorithm is capable of reducing the relative error of the identified dynamic loads by approximately 100%, with higher accuracy and noise immunity. In this study, the CGLS algorithm is used to identify the external dynamic loads acting on the sluice structure during the flood discharge process of a large hydropower station in China. Three equivalent dynamic loads acting on the pier and the bottom plate are obtained. The positive analysis of the vibration response of the discharge structure is conducted using the equivalent load. The results show that the error between the measured vibration response signals and the vibration response signals obtained from the positive analysis is relatively minor, with the exception of a few individual measurement points. The displacement response mean square error is less than 10%, thereby substantiating the accuracy and validity of the CGLS load identification method proposed in this study in the actual engineering.

Anomaly detection in bridge structural health monitoring via 1D-LBP and statistical feature fusion

Over the past few years, numerous bridges have been equipped with structural health monitoring (SHM) systems to continuously monitor critical structural parameters, enabling early detection of potential issues and timely maintenance. However, the monitoring data frequently contain anomalies due to various interferences during the acquisition and transmission processes. To address this, this paper proposes a robust and efficient anomaly detection and classification method. The method extracts one-dimensional local binary pattern (1D-LBP) features and time-domain statistical features from the monitoring data, fusing them into a comprehensive feature representation. These fused features are then input into an extreme gradient boosting (XG-Boost) classifier for anomaly detection. Additionally, a 1D-LBP feature simplification method is introduced to enhance detection efficiency. The effectiveness of the proposed method was validated using monitoring data from a long-span cable-stayed bridge SHM system. Experimental results demonstrate the excellent performance of the method in terms of detection accuracy and efficiency.

Triple Independently Tunable Sensing with Multiple Resonances Based on Metal-insulator-metal Waveguide

In this study, a novel plasmonic refractive index sensor is proposed, featuring a metal–insulator–metal (MIM) waveguide coupled with a composite cavity structure consisting of a double-arc rectangle cavity, a bridge cavity and a three-half-ring configuration cavity. Finite element simulations are used to analyze the transmission characteristics and magnetic field distributions. The results show that six resonance peaks can be produced by three independent resonators. Moreover, there is a good linear relationship between these resonance wavelengths and the structural parameters of the resonators. Then, the refractive index sensing performances of the structure are analyzed by changing the refractive index of the medium within the resonator. The highest sensitivities of the peaks in their resonators are 1732, 2800 and 4568 nm/RIU, respectively. In addition, the proposed structure is tested for the simultaneous detection of the peanut oil concentration of three different blended oils: soybean-peanut, peanut-rapeseed and peanut-sunflower. The ability of the sensor to accurately and simultaneously measure these different oil mixtures highlights its potential for biochemical sensing.

Study of forced convection heat transfer characteristics in modified structures derived from the Weaire-Phelan foam geometry

Weaire-Phelan (thereafter W-P) structures, as a foam-liked structures, were widely used in the field of flow and heat transfer. The effect of various methods for removing connecting struts on the flow and heat transfer characteristics of W-P structures was investigated, while maintaining constant porosity for all structures. Furthermore, the influence of porosity on the optimal strut removal scheme for W-P structures was examined. Computational fluid dynamics (CFD) was employed to analyse the flow and heat transfer characteristics of these structures. Additionally, four representative structures were created through 3D printing technology to experimentally validate the results. The effects of geometric structure parameters on the flow resistance and heat transfer characteristics of different structures were also analysed in depth. The findings indicate that all the proposed methods for removing connecting struts result in a reduction in pressure drop of up to 20.2 %. However, the most overall heat transfer performance (OHTP) was achieved when only the symmetry direction connecting struts were removed. Furthermore, as porosity decreases, the improvement in OHTP with the strut removal scheme becomes more significant compared to the original W-P. This study provides ideas for the optimisation of complex strut structures.

Study of hydraulic transport characteristics and erosion wear of twisted four-lobed pipe based on CFD-DEM

Pipeline hydraulic transportation is extensively utilized across diverse sectors, with enhancing the performance of pipeline hydrodynamic transport and minimizing erosion wear on the pipeline walls being essential for ensuring the stability of pipeline operations.This paper introduces a methodology for the hydraulic transport of a twisted four-lobed pipe, employing a numerical and erosion model developed through the CFD-DEM (computational fluid dynamics and discrete element) coupling approach. An experimental circulating flow platform is constructed for validation purposes. The performance of the pipe is assessed by analyzing key indices including fluid velocity, pressure drop, particle trajectory, and erosion wear. The results indicate that twisted four-lobed pipe enhances fluid flow rates, facilitating particle discharge and mitigating accumulation, with reduced wear compared to the twin twist triangle spiral pipe. The analysis of structural parameters’ impact on hydraulic conveyance is also presented. These findings offer theoretical insights for optimizing pipeline performance in hydraulic conveyance while minimizing wear.

Predicting the thermal conductivity of polymer composites with one-dimensional oriented fillers using the combination of deep learning and ensemble learning

Polymer composites with one-dimensional (1D) oriented fillers, recognized for their high thermal conductivity (TC), are extensively utilized in cooling electronic components. However, the prediction of the TC of composites with 1D oriented fillers poses a challenge due to the significant impact of filler orientation on composite TC. In this paper, we use a strategy that combines deep learning and ensemble learning to efficiently and quickly predict the TC of composites with 1D oriented fillers. First, as a control, we used convolutional neural network (CNN) model to predict the TC of 1D carbon fiber-epoxy composite, and the R-squared (R2) on the test set reached 0.924. However, for composites consist of different matrices and fillers, the CNN model needs to be retrained, which greatly wastes computing resources. Therefore, we define a descriptor ‘Orientation degree (Od)’ to quantitatively describe the spatial distribution of the 1D fillers. CNN model was used to predict this structural parameter, the accuracy R2 can reach 0.950. Using Od as a feature, random forest regression (RFR) was used to predict the TC, and the accuracy R2 reached 0.954, which was higher than that of CNN control group. We further successfully extended this strategy to composites consist of different 1D fillers and matrices, and only one CNN model and one RFR model needed to be trained to achieve fast and accurate TC prediction. This strategy provides valuable insights and guidance for machine learning-based material property prediction.

Multidisciplinary structural optimization of polysaccharides preventing alcohol-induced liver disease with computer-aided molecular design

Here, we optimized the active units of polysaccharides and investigated the conformational relationship between the polysaccharides and alcoholic liver disease (ALD) at the molecular level. We used data mining to screen polysaccharide structural parameters for ALD (PSP-ALD). Most ALD-resistant polysaccharides against ALD comprised glucose (Glc), mannose (Man), galactose (Gal), arabinose (Ara), and rhamnose (Rha). Additionally, (1 → 6)-, (1 → 3)-, and (1 → 4)- glycosidic linkages were mainly contained. Polysaccharides against ALD have a wide molecular weight distribution (2.1 × 103 Da - 9.6 × 107 Da). Based on the PSP-ALD analysis, six commercially available oligosaccharides were selected and their structures were built. After molecular docking, the binding affinities between stachyose and the key ALD targets were stronger, indicating that stachyose may be a polysaccharide-active unit against ALD (PAU-ALD). Furthermore, histological examination of liver tissue combined with serum levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), and triglycerides (TG) showed that stachyose had a significant protective effect against ALD in mice. In summary, we optimized a PAU-ALD and developed a method for studying the structure-activity relationship between polysaccharides and ALD at the molecular level, which provides a new research direction for the development and utilization of polysaccharides and their clinical applications in ALD.

Investigation on dynamic behaviors of the cage in cylindrical roller bearings based on nonlinear dynamic model with lubrication and cage whirling

The dynamic characteristics of the cages of cylindrical roller bearings (CRBs) significantly impact the service life of the bearings. This paper considers the lubrication between the roller and cage and the effect of cage whirling and establishes an accurate CRBs nonlinear dynamic model. On this basis, the correlation between the stability of the cage centroid trajectory, cage sliding characteristics, and bearing operating parameters is elaborated on. In addition, the influence of structural parameters, such as the number of rollers and pocket clearance, on the dynamic characteristics of the bearing system is also investigated. The results indicate that in the design stage of CRBs, it is essential to ensure a reasonable number of rollers and a smaller cage clearance ratio to reduce the slippage rate and enhance the stability of the bearing cage. Furthermore, during the usage stage of CRBs, changes in load or rotational speed operating parameters can lead to cage whirling. However, the stability of the cage whirling under the former condition is higher than that under the latter. Therefore, it is necessary to fully consider the reasonable rotational speed and load conditions to prevent premature damage to the cage.

Pneumonia detection on x-ray image using improved depthwise separable convolutional neural networks

A single neural network model cannot capture intricate and diverse features due to its ability to learn only a finite set of patterns from the data. Additionally, training and utilising a single model can be computationally demanding. Experts propose incorporating multiple neural network models to address these constraints to extract complementary attributes. Previous research has highlighted challenges network models face, including difficulties in effectively capturing highly detailed spatial features, redundancy in network structure parameters, and restricted generalisation capabilities. This study introduces an innovative neural network architecture that combines the Xception module with the inverse residue structure to tackle these issues. Considering this, the paper presents a model for detecting pneumonia in X-ray images employing an improved depthwise separable convolutional network. This network architecture integrates the inverse residual structure from the MobileNetV2 model, using the rectified linear unit (ReLU) non-linear activation function throughout the entire network. The experimental results show an impressive recognition rate with a test accuracy of 97.24% on the chest x-ray dataset. This method can extract more profound and abstract image features while mitigating overfitting issues and enhancing the network's generalisation capacity.