Dynamic Simulation of Vehicle Maneuvers for Loads Analysis

Presented at AIAA Aviation 2020 ForumTesting critical loads during specific dynamic maneuvers is essential to aircraft structural design, and several such dynamic load cases must be demonstrated during the certification process. A simulation capability is developed in this work to calculate critical loads on the vertical tail of a business jet resulting from yaw maneuvers required for certification. The data produced from these simulations can be used to inform future structural design decisions. Models for the pilot and flight control system are developed to simulate the pilot control actions needed to perform the maneuvers within the boundaries of pilot capabilities and flight control system limits. Aerodynamic and propulsive data are used to calculate the forces and moments on the aircraft and solve the 6-degree of freedom equations of motion to accurately model the aircraft’s trajectory. Sectional aerodynamic characteristics of the horizontal and vertical tail are used to calculate the structural loads at each section of the tail. The summation of these forces and moments yields the loads at the vertical tail root, which can be used to assess the structural design of the tail. The simulation is demonstrated on a T-tail business jet with three weight conditions and at flight conditions throughout the flight test envelope. The ultimate loading conditions and the number of control application cycles required to reach ultimate loads at the vertical tail are determined using the maneuver simulation

Identification of Patient Profiles with High Risk of Hospital Re-Admissions for Acute COPD Exacerbations (AECOPD) in France Using a Machine Learning Model

International audiencePurpose: To characterise patients with chronic obstructive pulmonary disease (COPD) who are rehospitalised for an acute exacerbation, to estimate the cost of these hospitalisations, to characterise high risk patient sub groups and to identify factors potentially associated with the risk of rehospitalisation. Patients and Methods: This was a retrospective study using the French National Hospital Discharge Database. All patients aged ≥40 years hospitalised for an acute exacerbation of COPD between 2015 and 2016 were identified and followed for six months. Patients with at least one rehospitalisation for acute exacerbation of COPD constituted the rehospitalisation analysis population. A machine learning model was built to study the factors associated with the risk of rehospitalisation using decision tree analysis. A direct cost analysis was performed from the perspective of national health insurance. Results: A total of 143,006 eligible patients were hospitalised for an acute exacerbation of COPD (AECOPD) in 2015-2016 (mean age: 74 years; 62.1% men). 25,090 (18.8%) were rehospitalised for another exacerbation within six months. In this study, 8.5% of patients died during or immediately following the index hospitalisation and 10.5% died during or immediately after rehospitalisation (p <0.001). The specific cost of these rehospitalisations was € 5304. The overall total cost per patient of all AECOPD-related stays was € 9623, being significantly higher in patients who were rehospitalised (€ 16,275) compared to those who were not (€ 8208). In decision tree analysis, the most important driver of rehospitalisation was hospitalisation in the previous two years (contributing 85% of the information). Conclusion: Rehospitalisations for acute exacerbations of COPD carry a high epidemiological and economic burden. Since hospitalisation for an acute exacerbation is the most important determinant of future rehospitalisations, management of COPD needs to focus on interventions aimed at decreasing the rehospitalisation risk of in order to lower the burden of disease

How are patients’ preferences for anti-TNF influenced by quality of life? A discrete choice experiment in Crohn’s disease patients

International audienc

Proposed Requirements for Cardiovascular Imaging Related Machine Learning Evaluation (PRIME) Checklist

International audienc

Proposed Requirements for Cardiovascular Imaging Related Machine Learning Evaluation (PRIME) Checklist

status: accepte