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Physics-informed neural networks (PINNs) have become a new tendency in flow simulation, because of their self-advantage of integrating both physical and monitored information of fields in solving the Navier–Stokes equation and its variants. In view of the strengths of PINN, this study aims to investigate the impact of spatially embedded data distribution on the flow field results around the train in the crosswind environment reconstructed by PINN.

PINN can integrate data residuals with physical residuals into the loss function to train its parameters, allowing it to approximate the solution of the governing equations. In addition, with the aid of labelled training data, PINN can also incorporate the real site information of the flow field in model training. In light of this, the PINN model is adopted to reconstruct a two-dimensional time-averaged flow field around a train under crosswinds in the spatial domain with the aid of sparse flow field data, and the prediction results are compared with the reference results obtained from numerical modelling.

The prediction results from PINN results demonstrated a low discrepancy with those obtained from numerical simulations. The results of this study indicate that a threshold of the spatial embedded data density exists, in both the near wall and far wall areas on the train’s leeward side, as well as the near train surface area. In other words, a negative effect on the PINN reconstruction accuracy will emerge if the spatial embedded data density exceeds or slips below the threshold. Also, the optimum arrangement of the spatial embedded data in reconstructing the flow field of the train in crosswinds is obtained in this work.

In this work, a strategy of reconstructing the time-averaged flow field of the train under crosswind conditions is proposed based on the physics-informed data-driven method, which enhances the scope of neural network applications. In addition, for the flow field reconstruction, the effect of spatial embedded data arrangement in PINN is compared to improve its accuracy.

Current methods for flow field reconstruction mainly rely on data-driven algorithms which require an immense amount of experimental or field-measured data. Physics-informed neural network (PINN), which was proposed to encode physical laws into neural networks, is a less data-demanding approach for flow field reconstruction. However, when the fluid physics is complex, it is tricky to obtain accurate solutions under the PINN framework. This study aims to propose a physics-based data-driven approach for time-averaged flow field reconstruction which can overcome the hurdles of the above methods.

A multifidelity strategy leveraging PINN and a nonlinear information fusion (NIF) algorithm is proposed. Plentiful low-fidelity data are generated from the predictions of a PINN which is constructed purely using Reynold-averaged Navier–Stokes equations, while sparse high-fidelity data are obtained by field or experimental measurements. The NIF algorithm is performed to elicit a multifidelity model, which blends the nonlinear cross-correlation information between low- and high-fidelity data.

Two experimental cases are used to verify the capability and efficacy of the proposed strategy through comparison with other widely used strategies. It is revealed that the missing flow information within the whole computational domain can be favorably recovered by the proposed multifidelity strategy with use of sparse measurement/experimental data. The elicited multifidelity model inherits the underlying physics inherent in low-fidelity PINN predictions and rectifies the low-fidelity predictions over the whole computational domain. The proposed strategy is much superior to other contrastive strategies in terms of the accuracy of reconstruction.

In this study, a physics-informed data-driven strategy for time-averaged flow field reconstruction is proposed which extends the applicability of the PINN framework. In addition, embedding physical laws when training the multifidelity model leads to less data demand for model development compared to purely data-driven methods for flow field reconstruction.

This paper aims to investigate the variations in the flow fields induced by transition regions in the windbreak structures between the flat ground and the cutting along a railway and to propose mitigation measures to improve the windproof ability of the windbreak.

The improved delayed detached eddy simulation method was used to simulate the impact of the windbreak transition on flow structures of the high-speed railway under different wind angles, and also the accuracy of the numerical results was validated with those of the wind tunnel test.

The results showed that the original windbreak transition region resulted in a dimensionless peak wind velocity of 0.62 and 0.82 for railway line-1 at wind angles of 90° and 75°, respectively, and the corresponding values were 0.81 and 0.97 for railway line-2. The flow structure analysis revealed the reason for the mismatched height in the transition region, and the right-angle structures of the windbreaks resulted in ineffective protection and sudden changes in the wind speed and direction. Two mitigation measures – oblique structure (OS) and circular curve structure (CCS) transition walls – were developed to reduce the peak wind speed. The OS provided superior protection. The peak value of dimensionless wind velocity was all less than 0.2 for OS and CCS.

The flow field deterioration mechanism induced by the inappropriate form of a windbreak transition at different wind angles was examined, and effective mitigation and improvement measures were proposed and compared with the original transition.

Based on the aerodynamic loads and dynamic performances of trains, this study aims to investigate the effect of crosswinds and raindrops on intercity trains operating on viaducts to ensure the safe operation of intercity railways in metropolitan areas.

An approach coupled with the Euler multiphase model as well as the standard k-ɛ turbulence model is used to investigate the coupled flow feature surrounding trains and viaducts, including airflow and raindrops, and the numerical results are validated with those of the wind tunnel test. Additionally, the train’s dynamic response and the operating safety region in different crosswind speeds and rainfall is investigated based on train’s aerodynamic loads and the train wheel–rail dynamics simulation.

The aerodynamic loads of trains at varying running speeds exhibit an increasing trend as the increase of wind speed and rainfall intensity. The motion of raindrop particles demonstrates a significant similarity with the airflow in wind and rain environments, as a result of the dominance of airflow and the supplementary impacts of droplets. As the train’s operating speed ranged between 120 and 200 km/h and within a rainfall range of 20–100 mm/h, the safe operating region of trains decreased by 0.56%–7.03%, compared with the no-rain condition (0 mm/h).

The impact of crosswind speeds and rainfall on the train’s aerodynamic safety is studied, including the flow feature of crosswind and different particle-sized raindrops around the train and viaduct, aerodynamic loads coefficients suffered by the intercity train as well as the operating safety region of intercity trains on the viaduct.

The purpose of this paper is to establish a dynamic analysis model of composite cylindrical roller bearings, investigate the effects of different working conditions on the kinematic characteristics of composite bearings and compare the differences between them and solid roller bearings.

This paper establishes a dynamic analysis model for composite cylindrical roller bearings and proves the correctness of the established model by establishing dynamic vibration experiments and contact theory for composite roller bearings. Comparative analysis was conducted on the effects of coupling changes in rotational speed, load, number of rollers and filling ratio on parameters such as bearing static stiffness, contact stress and vibration acceleration.

The composite roller can enhance the bearing’s operational stability and minimize contact stress, but that a higher filling ratio is going to increase the bearing’s stiffness. The acceleration degree of bearing vibration, the load on the outer raceway nodes and the bearing stability all decrease as inner ring speed rises.

A dynamic calculation model of composite cylindrical roller bearings is established, and the influence of multiparameter coupling changes on bearing vibration and contact is studied, which lays a foundation for the structural improvement of the bearings.

The purpose of this paper is to investigate the effects of minor addition of the rare earth (RE) element cerium, Ce, on the microstructures and creep properties of Sn-Ag-Cu solder alloys.

The pure Sn, Sn-Cu alloy, Sn-Ag alloy and Cu-Ce alloy were used as raw materials. Sn-Ag-Cu alloys with different contents of RE Ce were chosen to compare with Sn-Ag-Cu. The raw materials of Sn, Sn-Cu alloy, Sn-Ag alloy, Cu-Ce alloy were melted in a ceramic crucible, and were melted at 550°C ± 1°C for 40 minutes. To homogenize the solder alloy, mechanical stirring was performed every ten minutes using a glass rod. During the melting, KC1 + LiCI (1.3:1), were used over the surface of liquid solder to prevent oxidation. The melted solder was chill cast into a rod.

It is found that the microstructure exhibits smaller grains and the Ag₃Sn/Cu₆Sn₅ intermetallic compound (IMC) phases are modified in matrix with the addition of Ce. In particular, the addition of 0.03 wt.% Ce to the Sn-Ag-Cu solder can refine the microstructures and decrease the thickness of the IMC layers of Sn-Ag-Cu solder alloys. Meanwhile, thermodynamic analysis showed that these phenomena could be attributed to the reduction of the driving force for Cu-Sn IMC formation due to the addition of Ce. Results calculated using the thermodynamic method are close to the above experimental data. Thus, the optimum content of Ce in Sn-Ag-Cu solder alloys should be about 0.030 percent. Additionally, the effect of Ce on the creep rupture life of Sn-Ag-Cu soldered joints was studied. It was found that the creep rupture life may be increased up to 7.5 times more than that of the original Sn-Ag-Cu alloy, when Ce accounts for 0.030 percent.

This paper usefully investigates the effects of the RE cerium (Ce), on the microstructures and creep properties of Sn-Ag-Cu solder alloys, optimizing the quantity of Ce in the Sn-Ag-Cu solder alloy through a thermodynamic method and by creep-rupture life testing.

This paper aims to investigate the effects of deposition time on the structure and anti-corrosion properties of a micro-arc oxidation (MAO)/Al coating on AZ31B Mg alloy.

The study describes the fabrication of the coating via a combined process of MAO with multi-arc ion plating. The structure, composition and corrosion resistance of the coatings were evaluated using scanning electron microscopy, energy dispersive spectroscopy, X-ray diffraction and electrochemical methods.

The Al-layer is tightly deposited with a good mechanical interlock along the rough interface due to the Al diffusion. However, the Al layer reduces the anti-corrosion of MAO-coated Mg alloy because of structural defects such as droplets and cavities, which act as channels for corrosive media infiltration towards the substrate. Fortunately, the Al layer improves the substrate corrosion resistance owing to its passive behaviour, and the corrosion resistance can be enhanced with increasing deposition time. All results indicate that a buffer layer fabricated through the duplex process improves the interfacial compatibility between the hard coating and soft Mg alloys.

An MAO/Al duplex coating was fabricated via a combined process of MAO and physical vapour deposition. MAO/Al duplex coatings exhibit obviously passive behaviours on AZ31 Mg alloy. The structure and corrosion resistance of MAO/Al coatings were investigated.

The purpose of this paper is to numerically evaluate the reliability of SnAgCuCe solder joints compared with that of SnAgCu. A trace amount of the rare earth (RE) element Ce was added into SnAgCu solder in order to improve the reliability of lead‐free solder joints, which was evaluated based on finite element simulation and experiments.

A finite element method and an Anand constitutive model were employed to analyze the reliability of SnAgCuCe and SnAgCu solder joints in fine pitch quad flat packages under thermal cycling. The mechanical properties and reliability of solder joints were characterized by using thermal fatigue and creep tests, while the microstructure of the solder alloy and SnAgCu/SnAgCuCe solder joints were also investigated in the experimental procedure.

The simulation results indicated that SnAgCuCe solder joints had better reliability than SnAgCu. In addition, the experimental results accorded well with those of simulation, the thermal fatigue property and creep resistance of solder joints was increased by adding cerium. SnAgCuCe alloy can get its microstructure refinement improved and the thickness of the intermetallic compound layer at the solder/Cu interface decreased significantly compared to that of SnAgCu.

The findings provide certain guidelines to the reliability evaluation of solder joints when applying novel RE containing solder alloys in practical electronics industry applications. In the meantime, the reason for the superior reliability of SnAgCuCe solder joints can be explained from the property and microstructural point‐of‐view.

The Particle Swarm Optimization (PSO) method makes few or no assumptions about the optimization problem at hand and is applicable without much information about the problem. Although this fact constitutes one of the most important advantages of the PSO method, it can also be considered as a waste of available knowledge about the specific problem, which could have drastically improved the search performance. This paper aims to introduce an improvement to the PSO method such that the exploitation of any available knowledge about the specific optimization problem can be combined with the powerful blind‐search ability of the original method.

The improvement is achieved by the so‐called Knowledge Supported PSO (KS‐PSO), which consists of a combination of two modes: a mode that operates according to the original PSO approach and a knowledge‐based mode which the user has to design for the specific problem.

The application of the proposed KS‐PSO method is presented for two rather different optimization problems chosen from the domain of control and computer engineering: the model‐free tuning of a Fractional‐Order PID controller and the training of a single‐layer perceptron. The simulation results demonstrate the performance improvement in KS‐PSO as compared to the original PSO.

This paper presents a novel version of the well‐known PSO method, which achieves performance improvement by combining the original blind‐search capability with the exploitation of available knowledge about the specific problem.

The purpose of this paper is to study the relationship between high temperature oxidation and temperature rise rate of engine oil attempted to explore a new indicator to evaluate oil degradation.

Accelerated oxidation test combined with molecular simulation and road test is carried out in this paper. The temperature rise characteristics of mineral oil and synthetic oil under different oxidation temperatures (140°C, 155°C and 170°C) and time (50 h, 100 h, 150 h and 200 h) were determined by accelerated oxidation. The mechanism of temperature change characteristics of used oils was analyzed with molecular simulation. Two experimental vehicles carried six road tests with synthetic and mineral oil.

The results of this study show that the temperature rise rate of oxidized mineral and synthetic oil is higher than the new oil. The temperature rise rate is proportional to the oxidation time and oxidation temperature. The synthetic engine oil temperature rise rate is lower than that of the mineral engine oil. The same result was obtained in road tests. Molecular simulation verifies that small molecules were generated after oil oxidation which results in intermolecular friction and increased heat generation.

This paper indicates that temperature rise rate has potential to be taken as an indicator to evaluate oil oxidation which provides a new way for engine oil analysis.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0177/