Numerical comparisons of the aerodynamic performances of wind-tunnel train models with different inter-carriage gap spacings under crosswind
Introduction
HSTs (high-speed trains) operating under crosswind are surrounded by transient flow structures, which are random, chaotic, and full of turbulent eddies with different scales. A train immersed in such a turbulent flow is subjected to transient aerodynamic forces that determine the stability, safety, and efficiency of the HSTs (Dorigatti et al., 2015; Versteeg and Malalasekera, 2007). Therefore, numerous studies have been conducted on HSTs cruising under crosswind based on the reduced-scale wind tunnel tests and numerical simulations (Baker et al., 2004; Dorigatti et al., 2015; Niu et al., 2016; Schober et al., 2010; Sicot et al., 2018; Wang et al., 2018, Wang et al., 2018; Zhang et al., 2018b). Owing to the high authenticity and easily controlled experimental conditions, the reduced-scale wind tunnel experiment has almost become one of the most widely used experimental methods for the studies of HST aerodynamic performances (Bocciolone et al., 2008; Chen et al., 2018; Dorigatti et al., 2015; Li et al., 2018b; Niu et al., 2016; Zhang et al., 2018b).
When conducting wind tunnel tests on the multi-car marshaling HST models, such as the typical three-car marshaling HST model consisting of the head, middle, and tail cars, in order to separately test the detailed aerodynamic quantities of each train vehicle of the three-car train models, the head, middle, and tail car models cannot be connected through the internal and external windshields like the actual marshaling method of the operating HSTs. Thus, these three train vehicle models should be separately installed on the wind tunnel floor with a proper gap spacing between adjacent vehicle models to avoid the mechanical contact that causes inaccurate testing of the aerodynamic quantities of the high-speed train model. Zhang et al. (2018a, b) emphasized that in wind tunnel tests, an inter-carriage gap with a certain distance between adjacent vehicles should be adopted to determine the aerodynamic forces acting on individual vehicles and to avoid the mechanical force interference between adjacent vehicles. However, the appropriate gap spacing has not been given. Zhang et al. (2016) highlighted the internal and external windshields between the adjacent vehicle models were separated, not only to make independent and accurate measurements of the aerodynamic quantities for the HST model but also to guarantee the geometric similarity of the inter-carriage gap regions between the model and prototype trains. The gap spacing of 8 mm was adopted for the 1/8th-scale train model, however, without reasons for the gap spacing.
It should be noted that, for real HSTs, adjacent vehicles are connected by internal and external windshields with no gap spacing, while different gap spacings may be adopted in different wind tunnel tests. However, the theoretical basis for wind tunnel tests is based on the similarity criterion, and geometric similarity is essential to achieving complete similarity between the train model and prototype flow fields (Munson et al., 2009; CEN European Standard 14,067-6, 2010). The existing experiences have shown that relatively minor simplifications and distortions of the train model geometry will alter the airflow structures, resulting in substantial variation in the aerodynamic performances. (Munson et al., 2009; CEN European Standard 14,067-6, 2010). Thus, the differences between the gap spacings not only cause discrepancies between wind tunnel test results and an actual train, but also divergences between wind tunnel tests that adopt different gap spacings.
Under no crosswind, 10 different gap spacings were investigated by Li et al. (2019a). The results showed that the gap spacing should be smaller than 10 mm to obtain more realistic results for HSTs in the wind tunnel tests, however, in the study, different gap spacings were achieved by changing the spatial position of a certain vehicle longitudinally, which not only caused the variation of the gap spacing, but also that of the relative position of vehicles and the total length of the scale train models (the distance between the nose tips of the head and tail cars). These variations would lead to additional effects on the study results, in addition to the effects only caused by gap spacings. Thus, to avoid the additional effects caused by the variation of the relative position of vehicles and to focus on the pure effect of gap spacings, six different gap spacings obtained by installing windshields with different thicknesses on the vehicle end walls were systematically researched by Xia et al. (2020). The results showed that the airflow pattern and the aerodynamic loads acting on the vehicle parts surrounding the inter-carriage gap region significantly changed as the gap spacing increased, thus, the gap spacing should not exceed 8 mm to minimize the effect of gap spacings. In general, the aerodynamic performances of HSTs under crosswind are important content for wind tunnel experiments for reduced-scale train models. However, the studies mentioned above about the effects of gap spacings on the flow structures and aerodynamic performances of HST models did not consider the crosswind conditions, which gives impetus to systematically research the influence of gap spacings on HST models under crosswind.
In this study, the method used to achieve different gap spacings is the same as that adopted by Xia et al. (2020), which can be referred to for the details. The numerical simulations are conducted on the 1/8th-scale train models with five different gap spacings of 0, 5, 8, 10, and 20 mm under crosswind to investigate the aerodynamic effects of gap spacings on the HST models under crosswind. The train models and numerical simulation settings are introduced in section 2. Section 3 demonstrates the validation of the numerical methodology and analyses of the time-averaged and transient aerodynamic quantities and flow structures. Finally, the conclusions obtained are given in section 4.
Section snippets
Description of the train models and cases
The 1/8th-scale HST model applied in this study is depicted in Fig. 1. As shown in Fig. 1(b), the train model consists of the head, middle, and tail cars. Furthermore, a high geometric similarity between the train model and the prototype is guaranteed by retaining the important geometric structures such as the bogies, inter-carriage gaps, and obstacle deflector. The characteristic length scale for the model is the train height H, which is 3.7 m for a full-scale train and 0.4625 m in 1:8 scale.
Results validation
The numerical results conducted by Zhang et al. (2018b) were compared with the wind tunnel data to validate the reliability. The wind tunnel test was conducted on the 1/8th-scale HST model in three-car formation in the second test section with a section size of 8 m × 6 m (WT × HT) (shown as Fig. 8(a)) for the large-scale low-speed wind tunnel at the China Aerodynamic Research and Development Center. The main models applied in this wind tunnel test are illustrated in Fig. 8(b). The train model
Conclusion
The effect of inter-carriage gap spacings on the aerodynamic performances of the 1/8th-scale high-speed train model under crosswind was investigated using the numerical simulation based on IDDES with the SST k-omega model in this study. The following main conclusions can be drawn:
- (1)
Under crosswinds, the gap spacing has significant effects on different components of the time-averaged aerodynamic loads for different train vehicles. Compared with the zero gap spacing (0 mm), the existence of the
CRediT authorship contribution statement
Yutao Xia: Writing – original draft, Methodology, Software. Tanghong Liu: Supervision, Funding acquisition, Writing – review & editing. Wenhui Li: Validation, Resources, Writing – review & editing. Xiao Dong: Investigation, Visualization. Zhengwei Chen: Visualization. Zijian Guo: Investigation.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
The authors acknowledge the computational resources provided by the High-Speed Train Research Center of Central South University, China. This work was supported by the National Railway Administration of China (Grant No. 18T043), the National Railway Administration of China (Grant No. 2018Z035), and the Fundamental Research Funds for the Central Universities of Central South University (Grant No. 2021zzts0163 and 2021zzts0170).
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