Approximation of structural damping and input excitation force

Structural dynamic characteristics are the most significant parameters that play a decisive role in structural damage assessment. The more sensitive parameter to the damage is the damping behavior of the structure. The complexity of structural damping mechanisms has made this parameter to be one of the ongoing research topics. Despite all the difficulties in the modeling of damping, there are some approaches like as linear and nonlinear models which are described as the energy dissipation throughout viscous, material or structural hysteretic and frictional damping mechanisms. In the presence of a mathematical model of the damping mechanisms, it is possible to estimate the damping ratio from the theoretical comparison of the damped and un-damped systems. On the other hand, solving the inverse problem of the input force estimation and its distribution to each SDOFs, from the measured structural responses plays an important role in structural identification process. In this paper model-based damping approximation method and a modelless structural input estimation are considered. The effectiveness of proposed methods has been carded out through analytical and numerical simulation of the lumped mass system and the results are compared with reference data. Consequently, high convergence of the comparison results illustrates the satisfactory of proposed approximation methods.

Mechanical responses of pristine and defective hexagonal boron-nitride nanosheets:A molecular dynamics investigation

ABSTRACT In this work we conducted classical molecular dynamics(MD)simulation to investigate the mechanical characteristics and failure mechanism of hexagonal boron-nitride(h-BN)nanosheets.Pristine and defective structure of h-BN nanosheets were considered under the uniaxial tensile loadings at various temperatures.The defective structure contains three types of the most common initial defects in engineering materials that are known as cracks,notches(with various length/size),and point vacancy defects(with a wide range of concentration).MD simulation results demonstrate a high load-bearing capacity of extremely defective(amorphized)h-BN nanosheets.Our results also reveal that the tensile strength decline by increasing the defect content and temperature as well.Our MD results provide a comprehensive and useful vision concerning the mechanical properties ofh-BN nanosheets with/without defects,which is very critical for the designing of nanodevices exploiting the exceptional physics of h-BN.

Electronic and mechanical responses of two-dimensional HfS2,HfSe2,ZrS2,and ZrSe2 from first-principles

During the last decade, numerous high-quality two-dimensional (2D) materials with semiconducting electronic character have been synthesized. Recent experimental study (Sci. Adv. 2017;3: el700481) nevertheless confirmed that 2D ZrSe2 and HfSe2 are among the best candidates to replace the silicon in nanoelectronics owing to their moderate band-gap. We accordingly conducted first-principles calculations to explore the mechanical and electronic responses of not only ZrSe2 and HfSe2, but also ZrS? and HfS? in their single-layer and free-standing form. We particularly studied the possibility of engineering of the electronic properties of these attractive 2D materials using the biaxial or uniaxial tensile loadings. The comprehensive insight provided concerning the intrinsic properties of HfS2, HfSe2, ZrS2, and ZrSc? can be usefiil for their future applications in nanodevices.

A deep energy method for functionally graded porous beams

We present a deep energy method(DEM)to solve functionally graded porous beams.We use the EulerBernoulli assumptions with varying mechanical properties across the thickness.DEM is subsequently developed,and its performance is demonstrated by comparing the analytical solution,which was adopted from our previous work.The proposed method completely eliminates the need of a discretization technique,such as the finite element method,and optimizes the potential energy of the beam to train the neural network.Once the neural network has been trained,the solution is obtained in a very short amount of time.