Driving involves long hours of physical work within c onfined compartment. Taxi drivers usually work with prolonged working hours, add itional stress may likely be induced on particular body limbs. Occupational heal t...Driving involves long hours of physical work within c onfined compartment. Taxi drivers usually work with prolonged working hours, add itional stress may likely be induced on particular body limbs. Occupational heal th may occur and working efficiency may potentially be affected resulting fr om fatigues, pains or diseases. These problems, however, could be remedied if mo re attention is paid on seating design, the workplace and driving postures adopt ed. Ergonomics design can provide better understanding of those concerned areas. A study was conducted to analyse occupational and health problems related to ta xi drivers. Through ergonomic evaluation of driver’s compartment, the authors m ake recommendations to improve health and safety aspects. Besides, the experienc e of gained from this study using ergonomic design can be extended to other occu pation or products.展开更多
在汽车气动外形优化设计中,往往需要大量的高精度CFD数据作为支撑。然而,高精度CFD数据获取难度大、成本高。为了缓解汽车气动优化设计中气动特性评估精度和效率之间的矛盾,根据迁移学习与数据融合的思想,提出了一种基于多精度深度神经...在汽车气动外形优化设计中,往往需要大量的高精度CFD数据作为支撑。然而,高精度CFD数据获取难度大、成本高。为了缓解汽车气动优化设计中气动特性评估精度和效率之间的矛盾,根据迁移学习与数据融合的思想,提出了一种基于多精度深度神经网络(multi-fidelity deep neural network, MFDNN)的汽车外形优化设计方法,以减少优化设计中所需的高精度数据个数,从而有效提升优化速度、降低优化成本。将所发展的优化方法应用于快背式MIRA标准模型减阻优化设计中,优化结果表明,该方法能够充分融合不同精度数据所蕴含的知识,加速气动外形优化进程,提升优化效率。以收敛用时作为评价指标,在取得相近或更优优化结果的前提下,基于多精度神经网络的优化框架的收敛速度是基于单精度神经网络的离线优化框架的5.85倍,是基于单精度神经网络的在线优化框架的2.81倍。展开更多
文摘Driving involves long hours of physical work within c onfined compartment. Taxi drivers usually work with prolonged working hours, add itional stress may likely be induced on particular body limbs. Occupational heal th may occur and working efficiency may potentially be affected resulting fr om fatigues, pains or diseases. These problems, however, could be remedied if mo re attention is paid on seating design, the workplace and driving postures adopt ed. Ergonomics design can provide better understanding of those concerned areas. A study was conducted to analyse occupational and health problems related to ta xi drivers. Through ergonomic evaluation of driver’s compartment, the authors m ake recommendations to improve health and safety aspects. Besides, the experienc e of gained from this study using ergonomic design can be extended to other occu pation or products.
文摘在汽车气动外形优化设计中,往往需要大量的高精度CFD数据作为支撑。然而,高精度CFD数据获取难度大、成本高。为了缓解汽车气动优化设计中气动特性评估精度和效率之间的矛盾,根据迁移学习与数据融合的思想,提出了一种基于多精度深度神经网络(multi-fidelity deep neural network, MFDNN)的汽车外形优化设计方法,以减少优化设计中所需的高精度数据个数,从而有效提升优化速度、降低优化成本。将所发展的优化方法应用于快背式MIRA标准模型减阻优化设计中,优化结果表明,该方法能够充分融合不同精度数据所蕴含的知识,加速气动外形优化进程,提升优化效率。以收敛用时作为评价指标,在取得相近或更优优化结果的前提下,基于多精度神经网络的优化框架的收敛速度是基于单精度神经网络的离线优化框架的5.85倍,是基于单精度神经网络的在线优化框架的2.81倍。
