Current design of rectangular steel silos: limitations and improvement

Abstract This study proposes a modification for the current design approach for square and rectangular silos that accounts for silos’ wall flexibility. First, the authors investigated the effect of wall stiffness symbolized by the wall width-to-thickness ratio (a/t) and silo’s dimensions, on the wall-filling pressure using a recently validated 3D finite element model (FEM). The model was then employed to predict the pressures acting on silos’ walls accounting for the stress state in stored granular materials. Most design formulas and guidelines assume silos’ walls to be rigid. This assumption is acceptable for the case of rigid wall concrete silos; however, it is questionable for semi-rigid, flexible wall metal silos. Consequentially, it is crucial to determine the minimum wall stiffness necessary to secure the applicability of the current design rigid wall assumptions and to propose a way to deal with semi-rigid and flexible walls. To this end, several wall pressure distributions that correspond to filling steel silos with varied wall thicknesses were studied. A new adjustment to the Janssen technique was proposed for a better estimate of the wall-filling pressures for square and rectangular silos. In the case of prismatic silos, the Eurocode uses the Janssen equation together with an equivalent radius of a corresponding circular silo (with the same hydraulic radius) to determine the wall pressure. This method predicts pressure values that are practically accurate for rigid-wall silos, but its accuracy decreases for semi-rigid and flexible-wall silos. As an enhancement, the Janssen equation was modified in this research to generate more accurate pressure estimates based on the equivalent volume concept. The finite element results of several developed models with the same granular material were compared to the estimations of the newly established approach to verify the broad range of its applicability

Three-Dimensional Finite Element Analysis for Pressure on Flexible Wall Silos

A 3D finite element model (FEM) for predicting the distribution of lateral pressure in a square flexible walled steel silo during the filling phase was analyzed in this study. The numerical approach, developed using Abaqus software, predicts the stress state in bulk solids, as well as the pressures exerted on the silo walls. An elasto-plastic model using the Drucker–Prager criterion was employed to simulate the behavior of the granular materials. The FEM simulates the behavior of the bulk solid and its interaction with the silo’s wall and base using a surface-to-surface discretization model. The model’s predictions were validated by previous experimental measurements. The results revealed good agreement between the FEM predictions and the experimental measurements. The research confirms that the lateral pressure distribution is not uniform at any silo level. This highlights the fact that many available theories and current design codes are not accurate for flexible steel walls. As a result of the wall’s deformability, pressure regimes on the silo wall change significantly in the horizontal direction at any level. The results showed that the horizontal variations of lateral pressure change drastically with regard to wall stiffness. The FEM has been used to investigate the effect of critical parameters on wall pressure distribution, such as properties of bulk solids, wall thickness, and silo type, whether deep or flat

SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

Background: Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods: The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results: NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion: As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population