Chaokai Zhang , Feng Zhu , Wenye He , Zhiqing Cheng , Songbai Ji
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引用次数: 0
Abstract
The Advanced combat helmet (ACH) is critical for mitigating the risk of blast-induced traumatic brain injury (bTBI). Helmet foam pads are in continuous contact with the head to provide mechanical support. They are essential for helmet bTBI mitigation effectiveness and wearing comfort. In this study, we parametrically investigate the significance of foam pad thickness and relative density on reducing the peak intracranial pressure (ICP) from blast. In addition, we study how they influence the perceived comfort, by quantifying the distribution uniformity of ACH-to-scalp pressure resulting from gravity, referred to as the Comfort Index. Three specific pad thicknesses and random relative densities coupled with a range of trinitrotoluene (TNT) masses placed to the front or side of the helmet-head complex were used for simulation. The incidence pressures from the ConWep model were used as input for blast loading. The ratios between peak ICP in the corpus callosum and the peak incident pressure as well as the comfort indices were analyzed using a data-driven approach. A multi-functional design method, Pareto front, was used to identify sets of optimal parameters based on user preferred weighting factors for ICP reduction and head surface pressure distribution. Finally, a decision tree was applied to refine the rules for optimal designs. For an equal weighting on ICP reduction and surface pressure distribution, a pad thickness of 10 mm and relative density of 7.7 % were identified. This study demonstrates the effectiveness of combining Pareto front and decision trees for the identification of optimal design parameters for the ACH.
期刊介绍:
The objective of this journal is to communicate recent and projected advances in computer-based engineering techniques. The fields covered include mechanical, aerospace, civil and environmental engineering, with an emphasis on research and development leading to practical problem-solving.
The scope of the journal includes:
• Innovative computational strategies and numerical algorithms for large-scale engineering problems
• Analysis and simulation techniques and systems
• Model and mesh generation
• Control of the accuracy, stability and efficiency of computational process
• Exploitation of new computing environments (eg distributed hetergeneous and collaborative computing)
• Advanced visualization techniques, virtual environments and prototyping
• Applications of AI, knowledge-based systems, computational intelligence, including fuzzy logic, neural networks and evolutionary computations
• Application of object-oriented technology to engineering problems
• Intelligent human computer interfaces
• Design automation, multidisciplinary design and optimization
• CAD, CAE and integrated process and product development systems
• Quality and reliability.