Broadly accepted is that most knee injuries result from increased vertical forces,usually induced by an incidental ski fall,collision,or a high jump.We present a new non-contact knee injury mechanism that can happen d...Broadly accepted is that most knee injuries result from increased vertical forces,usually induced by an incidental ski fall,collision,or a high jump.We present a new non-contact knee injury mechanism that can happen during a ski turn.Such an injury is governed by a sudden inward turn of the inner ski and consequent swing of the inner leg followed by a nearly instant stop when locked by hip and knee joints.The model provides predictive results for a lateral tibial plateau compression fracture because several simplifications have been made.We confirmed that the modelled compression stresses at typical skiing conditions and with typical skiing equipment can provoke serious knee injuries.The awareness of skiers and skiing equipment industry of the described knee injury mechanism can act as an important injury-prevention factor.展开更多
In this study,we present for the first time the application of physics-informed neural network(PINN)to fretting fatigue problems.Although PINN has recently been applied to pure fatigue lifetime prediction,it has not y...In this study,we present for the first time the application of physics-informed neural network(PINN)to fretting fatigue problems.Although PINN has recently been applied to pure fatigue lifetime prediction,it has not yet been explored in the case of fretting fatigue.We propose a data-assisted PINN(DA-PINN)for predicting fretting fatigue crack initiation lifetime.Unlike traditional PINN that solves partial differential equations for specific problems,DA-PINN combines experimental or numerical data with physics equations as part of the loss function to enhance prediction accuracy.The DA-PINN method,employed in this study,consists of two main steps.First,damage parameters are obtained from the finite element method by using critical plane method,which generates a data set used to train an artificial neural network(ANN)for predicting damage parameters in other cases.Second,the predicted damage parameters are combined with the experimental parameters to form the input data set for the DA-PINN models,which predict fretting fatigue lifetime.The results demonstrate that DA-PINN outperforms ANN in terms of prediction accuracy and eliminates the need for high computational costs once the damage parameter data set is constructed.Additionally,the choice of loss-function methods in DA-PINN models plays a crucial role in determining its performance.展开更多
基金The study was financially supported by the state budget of the Slovenian Research Agency under grant P2-0095.
文摘Broadly accepted is that most knee injuries result from increased vertical forces,usually induced by an incidental ski fall,collision,or a high jump.We present a new non-contact knee injury mechanism that can happen during a ski turn.Such an injury is governed by a sudden inward turn of the inner ski and consequent swing of the inner leg followed by a nearly instant stop when locked by hip and knee joints.The model provides predictive results for a lateral tibial plateau compression fracture because several simplifications have been made.We confirmed that the modelled compression stresses at typical skiing conditions and with typical skiing equipment can provoke serious knee injuries.The awareness of skiers and skiing equipment industry of the described knee injury mechanism can act as an important injury-prevention factor.
基金China Scholarship Council,Grant/Award Number:202008130124National Natural Science Foundation of China,Grant/Award Number:12272270+1 种基金Shanghai Pilot Program for Basic ResearchSlovenian Research Agency research core funding,Grant/Award Number:P2-0095。
文摘In this study,we present for the first time the application of physics-informed neural network(PINN)to fretting fatigue problems.Although PINN has recently been applied to pure fatigue lifetime prediction,it has not yet been explored in the case of fretting fatigue.We propose a data-assisted PINN(DA-PINN)for predicting fretting fatigue crack initiation lifetime.Unlike traditional PINN that solves partial differential equations for specific problems,DA-PINN combines experimental or numerical data with physics equations as part of the loss function to enhance prediction accuracy.The DA-PINN method,employed in this study,consists of two main steps.First,damage parameters are obtained from the finite element method by using critical plane method,which generates a data set used to train an artificial neural network(ANN)for predicting damage parameters in other cases.Second,the predicted damage parameters are combined with the experimental parameters to form the input data set for the DA-PINN models,which predict fretting fatigue lifetime.The results demonstrate that DA-PINN outperforms ANN in terms of prediction accuracy and eliminates the need for high computational costs once the damage parameter data set is constructed.Additionally,the choice of loss-function methods in DA-PINN models plays a crucial role in determining its performance.
