Computational Modelling of Parachute Deployment to allow better Optimisation through Parachute Design

IrvinGQ在设计和制造降落伞和空投设备方面有着悠久的历史，主要用于军事市场。IrvinGQ已经开发了许多降落伞，使用经验设计原则和广泛的测试程序，以确保降落伞满足所需的性能特性。物理测试是一个昂贵且耗时的过程，IrvinGQ希望研究在他们的降落伞开发过程中使用更多的计算模拟和实验数据探索的有效性。该项目是（i）开发和验证一种计算建模方法来预测降落伞性能，例如下降率和携带质量，以改进现有的设计。计算模拟，最初考虑由IrvinGQ测试的缩放降落伞模型，之后将模拟完整模型，以通过设计更改识别和指导性能优化功能。此外，在公司现有的全尺寸降落伞数据的支持下，（ii）将开发一种机器学习工具，用于未来的设计优化和新降落伞的性能增强。

IrvinGQ have a long history designing and manufacturing parachutes and aerial delivery equipment, primarily for the military market. IrvinGQ have developed many parachutes using empirical design principles and extensive testing programmes to ensure that parachutes meet the required performance characteristics. Physical testing is an expensive and time-consuming process and IrvinGQ want to research the validity of using more computational simulation and experimental data explorations in their parachute development process.This project is to (i) develop and validate a computational modelling methodology to predict parachute performance, such as rate of decent and carried mass, to improve on existing designs. The computational simulation, considered initially on a scaled parachute model being tested by IrvinGQ, and will simulate a full model afterwards to identify and guide performance optimisation features through design changes. In addition, and supported by company available full-scale parachute data, (ii) a machine learning tool will be developed for future design optimisation and enhanced performance of new parachutes.