Experimental and numerical investigation of mechanical behavior of plain woven CFRP composites subjected to three-point bending

The mechanical behavior of plain woven Carbon Fiber-Reinforced Polymer (CFRP) composites under Three-Point Bending (TPB) is investigated via experimental and numerical approaches. Multiscale models, including microscale, mesoscale and macroscale models, have been developed to characterize the TPB strength and damages. Thereinto, Representative Volume Elements (RVEs) of the microscale and mesoscale structures are established to determine the effective properties of carbon-fiber yarn and CFRP composites, respectively. Aimed at accurately and efficiently predicting the TPB behavior, an Equivalent Cross-Ply Laminate (ECPL) cell is proposed to simplify the inherent woven architecture, and the effective properties of the subcell are computed using a local homogenization approach. The macroscale model of the TPB specimen is constructed by a topology structure of ECPL cells to predict the mechanical behavior. The TPB experiments have been performed to validate the multiscale models. Both the experimental and numerical results reveal that delamination mainly appears in the top and bottom interfaces of the CFRP laminates. And matrix cracking and delamination are identified as the significant damage modes during the TPB process. Finally, the quasi-static and dynamic behaviors of plain woven composites are discussed by comparing the results of Low-Velocity Impact (LVI) and TPB simulations.     

Local empirical model of ionospheric variability

The paper presents an analysis of the ionospheric variability as a function of local time, month, and geomagnetic activity level. The 2003–2020 dataset of peak electron densities (NmF2) from the Irkutsk DPS-4 Digisonde (52.3°N, 104.3°E) was converted into the dataset of the NmF2 disturbances (ΔNmF2) representing the relative (percentage) deviations of the NmF2 from the 27-day running median. The ΔNmF2 dataset was used to calculate root mean square values of ΔNmF2 (σNmF2) by 27-day running averaging. These σNmF2 values were considered as a measure of ionospheric variability. The σNmF2 as function of local time, day of year, and year was the input for building the local empirical model of ionospheric variability based on the linear regression of σNmF2 on the 27-day average daily Ap-index of geomagnetic activity. The paper demonstrates the diurnal-seasonal variations in σNmF2 under low geomagnetic activity (linear regression intercept) as well as the rate of increase/decrease in σNmF2 with increasing Ap (linear regression slope). The obtained diurnal, seasonal, and geomagnetic activity behavior of σNmF2 is compared with previous studies of ionospheric variability.     

Macro-phenomenological high-strain-rate elastic fracture model for ice-impact simulations

The ice impact can cause a severe damage to an aircraft’s exposed structure, thus, requiring its prevention. The numerical simulation represents an effective method to overcome this challenge. The establishment of the ice material model is critical. However, ice is not a common structural material and exhibits an extremely complex material behavior. The material models of ice reported so far are not able to accurately simulate the ice behavior at high strain rates. This study proposes a novel high-precision macro-phenomenological elastic fracture model based on the brittle behavior of ice at high strain rates. The developed model has been compared with five reported models by using the smoothed particle hydrodynamics method so as to simulate the ice-impact process with respect to the impact speeds and ice shapes. The important metrics and phenomena (impact force history, deformation and fragmentation of the ice projectile and deflection of the target) were compared with the experimental data reported in the literature. The findings obtained from the developed model are observed to be most consistent with the experimental data, which demonstrates that the model represents the basic physics and phenomena governing the ice impact at high strain rates. The developed model includes a relatively fewer number of material parameters. Further, the used parameters have a clear physical meaning and can be directly obtained through experiments. Moreover, no adjustment of any material parameter is needed, and the consumption duration is also acceptable. These advantages indicate that the developed model is suitable for simulating the ice-impact process and can be applied for the anti-ice impact design in aviation.     

Adjoint-based robust optimization design of laminar flow wing under flight condition uncertainties

It is an inherent uncertainty problem that the application of laminar flow technology to the wing of large passenger aircraft is affected by flight conditions. In order to seek a more robust natural laminar flow control effect, it is necessary to develop an effective optimization design method. Meanwhile, attention must be given to the impact of crossflow(CF) instability brought on by the sweep angle. This paper constructs a robust optimization design framework based on discrete adjoint methods ...     

Orbital error propagation considering atmospheric density uncertainty

Due to the influence of various errors, the orbital uncertainty propagation of artificial celestial objects while orbit prediction is required, especially in some applications such as conjunction analysis. In the orbital error propagation of artificial celestial objects in low Earth orbits (LEOs), atmospheric density uncertainty is one of the important factors that require special attention. In this paper, on the basis of considering the uncertainties of position and velocity, the atmospheric density uncertainty is also taken into account to further investigate the orbital error propagation of artificial celestial objects in LEOs. Artificial intelligence algorithms are introduced, the MC Dropout neural network and the heteroscedastic loss function are used to realize the correction of the empirical atmospheric density model, as well as to provide the quantification of model uncertainty and input uncertainty for the corrected atmospheric densities. It is shown that the neural network we built achieves good results in atmospheric density correction, and the uncertainty quantization obtained from the neural network is also reasonable. Moreover, using the Gaussian mixture model - unscented transform (GMM-UT) method, the atmospheric density uncertainty is taken into account in the orbital uncertainty propagation, by adding a sampled random term to the corrected atmospheric density when calculating atmospheric density. The feasibility of the GMM-UT method considering atmospheric density uncertainty is proved by the further comparison of abundant sampling points and GMM-UT results (with and without considering atmospheric density uncertainty).