A new energy-based life prediction framework for calculation of axial and bending fatigue results at various stress ratios has been developed. The purpose of the life prediction framework is to assess the behavior of materials used in gas turbine engines, such as Titanium 6Al-4V (Ti 6Al-4V) and Aluminum 6061-T6 (Al 6061-T6). The work conducted to develop this energy-based framework consists of the following entities: (1) a new life prediction criterion for axial and bending fatigue at various stress ratios for Al 6061-T6, (2) the use of the previously developed improved uniaxial energy-based method to acquire fatigue life prior to endurance limit region (Scott-Emuakpor et al., 2007, “Development of an Improved High Cycle Fatigue Criterion,” ASME J. Eng. Gas Turbines Power, 129, pp. 162–169), (3) and the incorporation of a probabilistic energy-based fatigue life calculation scheme to the general uniaxial life criterion (the first entity of the framework), which is capable of constructing prediction intervals based on a specified percent confidence level. The precision of this work was verified by comparison between theoretical approximations and experimental results from recently acquired Al 606-T6 and Ti 6Al-4V data. The comparison shows very good agreement, thus validating the capability of the framework to produce accurate uniaxial fatigue life predictions for commonly used gas turbine engine materials.

ObjectivesTo develop and evaluate a machine learning model for predicting patient with trauma mortality within the US emergency departments.MethodsThis was a retrospective prognostic study using deidentified patient visit data from years 2007 to 2014 of the National Trauma Data Bank. The predictive model intelligence building process is designed based on patient demographics, vital signs, comorbid conditions, arrival mode and hospital transfer status. The mortality prediction model was evaluated on its sensitivity, specificity, area under receiver operating curve (AUC), positive and negative predictive value, and Matthews correlation coefficient.ResultsOur final dataset consisted of 2 007 485 patient visits (36.45% female, mean age of 45), 8198 (0.4%) of which resulted in mortality. Our model achieved AUC and sensitivity-specificity gap of 0.86 (95% CI 0.85 to 0.87), 0.44 for children and 0.85 (95% CI 0.85 to 0.85), 0.44 for adults. The all ages model characteristics indicate it generalised, with an AUC and gap of 0.85 (95% CI 0.85 to 0.85), 0.45. Excluding fall injuries weakened the child model (AUC 0.85, 95% CI 0.84 to 0.86) but strengthened adult (AUC 0.87, 95% CI 0.87 to 0.87) and all ages (AUC 0.86, 95% CI 0.86 to 0.86) models.ConclusionsOur machine learning model demonstrates similar performance to contemporary machine learning models without requiring restrictive criteria or extensive medical expertise. These results suggest that machine learning models for trauma outcome prediction can generalise to patients with trauma across the USA and may be able to provide decision support to medical providers in any healthcare setting.

This article presents assessment of the mechanical behavior of U-10wt% Mo (U10Mo) alloy based monolithic fuel plates subject to irradiation. Monolithic, plate-type fuel is a new fuel form being developed for research and test reactors to achieve higher uranium densities within the reactor core to allow the use of low-enriched uranium fuel in high-performance reactors. Identification of the stress/strain characteristics is important for understanding the in-reactor performance of these plate-type fuels. For this work, three distinct cases were considered: (1) fabrication induced residual stresses (2) thermal cycling of fabricated plates; and finally (3) transient mechanical behavior under actual operating conditions. Because the temperatures approach the melting temperature of the cladding during the fabrication and thermal cycling, high temperature material properties were incorporated to improve the accuracy. Once residual stress fields due to fabrication process were identified, solution was used as initial state for the subsequent simulations. For thermal cycling simulation, elasto-plastic material model with thermal creep was constructed and residual stresses caused by the fabrication process were included. For in-service simulation, coupled fluid-thermal-structural interaction was considered.First, temperature field on the plates was calculated and this field was used to compute the thermal stresses. For time dependent mechanical behavior, thermal creep of cladding, volumetric swelling and fission induced creep of the fuel foil were considered. The analysis showed that the stresses evolve very rapidly in the reactor. While swelling of the foil increases the stress of the foil, irradiation induced creep causes stress relaxation.

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