Artificial neural networks for predicting mechanical properties of crystalline polyamide12 via molecular dynamics simulations

Predicting material properties of 3D printed polymer products is a challenge in additive manufacturing due to the highly localized and complex manufacturing process. The microstructure of 3D printed products is fundamentally different from the ones obtained by using conventional manufacturing processes, which makes the task even more difficult. As a first step of a systematic multiscale approach, in this work, we have developed an artificial neural network (ANN) to predict the mechanical properties of the crystalline form of Polyamide12 (PA12) based on data collected from molecular dynamics simulations. Using the machine learning approach, we are able to predict the stress-strain relations of PA12 once the information of the macroscale deformation gradient is provided as the input parameter to the ANN. We have shown that this is an efficient and accurate approach, which can provide a three-dimensional molecular-level anisotropic stress-strain relation of PA12 for any macroscale mechanics model, such as finite element modeling at arbitrary quadrature points.Comment: Submitted to Microstructures (Revised

Mesh-free simulation of complex LCD geometries

We use a novel mesh-free simulation approach to study the post aligned bistable nematic (PABN) cell. By employing the Qian-Sheng formalism for liquid crystals along with a smooth representation of the surface posts, we have been able to identify two distinct stable configurations. The three-dimensional order field configurations of these states and their elastic free energies are consistent with both experimental results and previous simulation attempts. However, alternative states suggested in previous studies do not appear to remain stable when finite post curvature is considered.</p

The influence of model and measurement uncertainties on damage detection of experimental structures through recursive algorithms

In this work, we developed a framework for identifying frame-type structures regarding the measurement uncertainty and the uncertainty involved in inherent and structural parameters. The identification process is illustrated and examined on a one-eight-scale four-story moment-resisting steel frame under seismic excitation using two well-known recursive schemes: the Extended Kalman filter (EKF) and Unscented Kalman Filter (UKF) methods. The nonlinear system equations were assessed by applying a first-order instantaneous linearization approach through the EKF method. In contrast, the UKF algorithm employs several sample points to estimate moments of random variables’ nonlinear transformations. A nonlinear transformation is applied to distribute sample points to derive the precise mean and covariance up to the second order of any nonlinearity. Accordingly, it is theoretically expected that the UKF algorithm is more capable of identifying the nonlinear systems and determining the unknown parameters than the EKF algorithm. The capability of the EKF and UKF algorithms was assessed by considering a 4-story moment-resisting steel frame with several inherent uncertainties, including the material behavior model, boundary conditions, and constraints. In addition to these uncertainties, the combination of acceleration and displacement responses of different structural levels is employed to evaluate the capability of the algorithms. The information entropy measure is used to investigate further the uncertainty of a group of established model parameters. As highlighted, a good agreement is observed between the results using the information entropy measure criterion and those using the UKF and EKF algorithms. The results illustrate that using the responses of fewer levels placed in the proper positions may lead to improved outcomes than those of more improperly positioned levels

Application and modelling of shape-memory alloys for structural vibration control : state-of-the-art review

One of the most essential components of structural design for civil engineers is to build a system that is resistant to environmental conditions such as harsh chemical environments, and catastrophic disasters like earthquakes and hurricanes. Under these circumstances and disturbances, conventional building materials such as steel and concrete may demonstrate inadequate performance in the form of corrosion, deterioration, oxidizing, etc. Shape Memory Alloys (SMAs) are novel metals with distinct features and desirable potential to overcome the inadequacies of existing construction materials and enable the structure to tolerate disturbances more efficiently. Shape Memory Effect (SME) and Pseudoelasticity (PE) have been the most attractive characteristics that scientists have focused on among the various features that SMAs exhibit. The SME enables the material to retain its original shape after severe deformation, whereas the PE behaviour of SMAs provides a wide range of deformation while mitigating a substantial amount of susceptible stresses. These behaviours are the consequence of the phase transformation between austenite and martensite. Many investigations on the modelling and application of SMAs in structural systems to endure applied dynamic loadings in the form of active, passive, and hybrid vibration control systems have been undertaken. The focus of this paper is to present an overview of the SMA-based applications and most frequently employed constitutive modelling, as well as their limits in structural vibration control and seismic isolation devices