Analysis and prediction of high-speed train wheel wear based on SIMPACK and backpropagation neural networks

As train running speeds increase, the wheel-rail interactions of high-speed trains are becoming more complicated, and predicting and monitoring wheel wear are becoming increasingly important for the safe operation of high-speed trains. Therefore, identifying the critical factors that affect the wear of wheel-rail interactions and developing novel methods to predict wheel wear are of great importance. In this work, SIMPACK is used to establish a dynamic model of a high-speed train and to investigate the normal and lateral contact forces of the wheel-rail interfaces and the wear of the wheels for a train passing through a specially designed route that consists of straight-line, smooth-curved, and circular tracks. The wheel wear is predicted by means of the Archard wear model based on the SIMPACK analysis, and the wear is validated by a backpropagation neural network (BPNN) classification based on daily measured data provided by the Beijing Railway Administration. The results from the SIMPACK dynamic simulation and the BPNN classification show that the position of a wheel in a bogie has a significant effect on the wheel wear, but the position of a carriage in a train does not have a significant effect on the wheel wear. The findings from this study are very useful for the maintenance and safe operation of high-speed trains

Phantom-based quality assurance for multicenter quantitative MRI in locally advanced cervical cancer

Background and purpose: A wide variation of MRI systems is a challenge in multicenter imaging biomarker studies as it adds variation in quantitative MRI values. The aim of this study was to design and test a quality assurance (QA) framework based on phantom measurements, for the quantitative MRI protocols of a multicenter imaging biomarker trial of locally advanced cervical cancer. Materials and methods: Fifteen institutes participated (five 1.5 T and ten 3 T scanners). Each institute optimized protocols for T2, diffusion-weighted imaging, T1, and dynamic contrast-enhanced (DCE–)MRI according to system possibilities, institutional preferences and study-specific constraints. Calibration phantoms with known values were used for validation. Benchmark protocols, similar on all systems, were used to investigate whether differences resulted from variations in institutional protocols or from system variations. Bias, repeatability (%RC), and reproducibility (%RDC) were determined. Ratios were used for T2 and T1 values. Results: The institutional protocols showed a range in bias of 0.88–0.98 for T2 (median %RC = 1%; %RDC = 12%), −0.007 to 0.029 × 10−3 mm2/s for the apparent diffusion coefficient (median %RC = 3%; %RDC = 18%), and 0.39–1.29 for T1 (median %RC = 1%; %RDC = 33%). For DCE a nonlinear vendor-specific relation was observed between measured and true concentrations with magnitude data, whereas the relation was linear when phase data was used. Conclusion: We designed a QA framework for quantitative MRI protocols and demonstrated for a multicenter trial for cervical cancer that measurement of consistent T2 and apparent diffusion coefficient values is feasible despite protocol differences. For DCE–MRI and T1 mapping with the variable flip angle method, this was more challenging