Facoltà di Ingegneria, Università di Bologna, Italy.
Philos Trans A Math Phys Eng Sci. 2010 Jun 13;368(1920):2725-63. doi: 10.1098/rsta.2010.0046.
Bone biomechanics have been extensively investigated in the past both with in vitro experiments and numerical models. In most cases either approach is chosen, without exploiting synergies. Both experiments and numerical models suffer from limitations relative to their accuracy and their respective fields of application. In vitro experiments can improve numerical models by: (i) preliminarily identifying the most relevant failure scenarios; (ii) improving the model identification with experimentally measured material properties; (iii) improving the model identification with accurately measured actual boundary conditions; and (iv) providing quantitative validation based on mechanical properties (strain, displacements) directly measured from physical specimens being tested in parallel with the modelling activity. Likewise, numerical models can improve in vitro experiments by: (i) identifying the most relevant loading configurations among a number of motor tasks that cannot be replicated in vitro; (ii) identifying acceptable simplifications for the in vitro simulation; (iii) optimizing the use of transducers to minimize errors and provide measurements at the most relevant locations; and (iv) exploring a variety of different conditions (material properties, interface, etc.) that would require enormous experimental effort. By reporting an example of successful investigation of the femur, we show how a combination of numerical modelling and controlled experiments within the same research team can be designed to create a virtuous circle where models are used to improve experiments, experiments are used to improve models and their combination synergistically provides more detailed and more reliable results than can be achieved with either approach singularly.
骨生物力学在过去得到了广泛的研究,包括体外实验和数值模型。在大多数情况下,两种方法都有选择,而没有利用协同作用。实验和数值模型都存在与其准确性和各自应用领域相关的局限性。体外实验可以通过以下方式改进数值模型:(i)初步确定最相关的失效场景;(ii)通过实验测量的材料特性改进模型识别;(iii)通过准确测量的实际边界条件改进模型识别;以及(iv)基于从与建模活动同时进行的物理标本中直接测量的机械性能(应变、位移)提供定量验证。同样,数值模型可以通过以下方式改进体外实验:(i)在无法在体外复制的许多运动任务中确定最相关的加载配置;(ii)确定体外模拟的可接受简化;(iii)优化换能器的使用,以最大程度地减少误差并在最相关的位置提供测量;以及(iv)探索各种不同的条件(材料特性、界面等),这将需要巨大的实验工作量。通过报告对股骨的成功研究实例,我们展示了如何在同一个研究团队中结合数值建模和受控实验,以创建一个良性循环,其中模型用于改进实验,实验用于改进模型,它们的组合协同提供了比单独使用任何一种方法更详细和更可靠的结果。