Multi-Criteria Evaluation of the Failure of CFRP Laminates for Frames in the Automotive Industry.

Methods to predict the fracture of thin carbon fibre-reinforced polymers (CFRPs) under load are of great interest in the automotive industry. The manufacturing of composites involves a high risk of defect occurrence, and the identification of those that lead to failure increases the functional reliability and decreases costs. The performance of CFRPs can be significantly reduced in assembled structures containing stress concentrators. This paper presents a hybrid experimental-numerical method based on the Tsai-Hill criterion for behavior of thin CFRPs at complex loadings that can emphasize the threshold of stress by tracing the σ-τ envelope. Modified butterfly samples were made for shearing, traction, or shearing-with-traction tests in the weakened section by changing the angle of force application α. ANSYS simulations were used to determine the zones of maximum stress concentration. For thin CFRP samples tested with stacking sequences [0]8 and [(45/0)2]s, the main mechanical characteristics have been determined using a Dynamic Mechanical Analyzer (DMA) and ultrasound tests. A modified Arcan device (AD) was used to generate data in a biaxial stress state, leading to the characterization of the material as a whole. The generated failure envelope allows for the prediction of failure for other combinations of normal and shear stress, depending on the thickness of the laminations, the stacking order, the pretension of the fasteners, and the method used to produce the laminations. The experimental data using AD and the application of the Tsai-Hill criterion serve to the increase the safety of CFRP components.

Designing grazing susceptibility to land degradation index (GSLDI) in hilly areas.

Evaluation of grazing impacts on land degradation processes is a difficult task due to the heterogeneity and complex interacting factors involved. In this paper, we designed a new methodology based on a predictive index of grazing susceptibility to land degradation index (GSLDI) built on artificial intelligence to assess land degradation susceptibility in areas affected by small ruminants (SRs) of sheep and goats grazing. The data for model training, validation, and testing consisted of sampling points (erosion and no-erosion) taken from aerial imagery. Seventeen environmental factors (e.g., derivatives of the digital elevation model, small ruminants' stock), and 55 subsequent attributes (e.g., classes/features) were assigned to each sampling point. The impact of SRs stock density on the land degradation process has been evaluated and estimated with two extreme SRs' density scenarios: absence (no stock), and double density (overstocking). We applied the GSLDI methodology to the Curvature Subcarpathians, a region that experiences the highest erosion rates in Romania, and found that SRs grazing is not the major contributor to land degradation, accounting for only 4.6%. This methodology could be replicated in other steep slope grazing areas as a tool to assess and predict susceptible to land degradation, and to establish common strategies for sustainable land-use practices.

Environmental assessment of agricultural activities and groundwater nitrate pollution susceptibility: a regional case study (Southwestern Romania).

Diffuse pollution of water resources from agricultural sources is a major environmental issue in Europe. The nutrients released in groundwater from cultivated fields and livestock production, together with pesticides, are the main source of concern in the framework of the European Nitrates Directive (91/676/EEC). Southern Romania continues to represent one of the most important cereal production areas of the country. The intensive exploitation during the communist period continues to have repercussions for the precarious quality of groundwater. The aim of our study was to establish the environmental conditions, quantify the agricultural activities at the local administrative unit level and afterwards, to highlight areas of susceptibility to nitrate pollution of groundwater within the Oltenia Plain. One of the most efficient methods to evaluate human influences by agricultural activities on groundwater is using different types of indicators, such as land use indicators (cultivated surfaces), animal husbandry indicators (livestock and great beef units), and agri-environmental indicators (use of fertilisers based on nitrogen and phosphorus, quantity/ha). Throughout the paper, GIS methods are used to determine the degree of influence on nitrate pollution of several eco-pedological indicators: soil types and subtypes, slope of the land, soil texture, soil permeability, and groundwater level. Statistics indicate that 85% of the study area is susceptible to nitrate pollution from agriculture. Indicators provide information that can be easily interpreted by decision and policy makers, and they facilitate the process of reducing nitrate pollution. This study shows that the correlation of statistics and GIS modelling is a useful method for guiding prevention practices for groundwater pollution at the regional scale in Southwestern Romania.