Electrical and Mechanical Behaviour of Copper Tufted CFRP Composite Joints

Electrical continuity of dissimilar joints controls the current and thermal pathways during lightning strike. Tufting using carbon, glass or Kevlar fibres is a primary to introduce through thickness reinforcement for composite structures and assemblies. Replacing the conventional tuft thread material with metallic conductive wire presents an opportunity for enhancing current dissipation and deal with electrical bottlenecks across dissimilar joints. Simulation of the electro-thermo-mechanical behaviour of joints was carried out to assess the influence of metallic tufting. The finite element solver MSC.Marc was utilised. Mechanical models incorporate continuum damage mechanics (CDM) to capture progressive damage in both composite and aluminium components of the joint. The mechanical models were coupled with electrical and thermal simulations of reference and copper tufted carbon fibre epoxy composite joints to assess both the lightning strike response and mechanical robustness of the assembly as well as the improvements offered by tufting. Validation of the model is based on electrical conduction and temperature measurements alongside delamination tests.Clean Sky 2 Joint Undertaking under the European Union’s Horizon 2020 research and innovation programme under grant agreement No 887042, D-JOINTS

Electrical and mechanical behaviour of copper tufted CFRP composite joints

Electrical continuity of dissimilar joints controls the current and thermal pathways during lightning strike. Tufting using carbon, glass or Kevlar fibres is a primary to introduce through thickness reinforcement for composite structures and assemblies. Replacing the conventional tuft thread material with metallic conductive wire presents an opportunity for enhancing current dissipation and deal with electrical bottlenecks across dissimilar joints. Simulation of the electro-thermo-mechanical behaviour of joints was carried out to assess the influence of metallic tufting. The finite element solver MSC.Marc was utilised. Mechanical models incorporate continuum damage mechanics (CDM) to capture progressive damage in both composite and aluminium components of the joint. The mechanical models were coupled with electrical and thermal simulations of reference and copper tufted carbon fibre epoxy composite joints to assess both the lightning strike response and mechanical robustness of the assembly as well as the improvements offered by tufting. Validation of the model is based on electrical conduction and temperature measurements alongside delamination tests.European Union funding: 88704

Optimisation of an in-process lineal dielectric sensor for liquid moulding of carbon fibre composites

A dielectric sensor appropriate for process monitoring of carbon fibre composites manufacturing has been optimised and implemented in Resin Transfer Moulding (RTM). The sensor comprises a pair of twisted insulated copper wires and can be adapted to monitor both flow and cure. To simulate the dielectric response of the sensor, an electric field model was developed. The model was coupled with a multi-objective optimisation genetic algorithm to optimise the sensor design. The optimisation showed that increasing wire radius and decreasing coating thickness increases sensor sensitivity. Different sensor designs were implemented and used in a series of RTM trials to validate the technology in industrial conditions. The sensor operated successfully at pressures up to 7 bar and temperatures up to 180°C. A low diameter sensor using copper wire coated with polyimide showed the best response monitoring flow with an accuracy of 95%, whilst also following the cure and identifying vitrificatio

Seismic Loading for FAST: May 2011 - August 2011

As more wind farms are constructed in seismically active regions, earthquake loading increases in prominence for design and analysis of wind turbines. Early investigation of seismic load tended to simplify the rotor and nacelle as a lumped mass on top of the turbine tower. This simplification allowed the use of techniques developed for conventional civil structures, such as buildings, to be easily applied to wind turbines. However, interest is shifting to more detailed models that consider loads for turbine components other than the tower. These improved models offer three key capabilities in consideration of base shaking for turbines: 1) The inclusion of aerodynamics and turbine control; 2) The ability to consider component loads other than just tower loads; and 3) An improved representation of turbine response in higher modes by reducing modeling simplifications. Both experimental and numerical investigations have shown that, especially for large modern turbines, it is important to consider interaction between earthquake input, aerodynamics, and operational loads. These investigations further show that consideration of higher mode activity may be necessary in the analysis of the seismic response of turbines. Since the FAST code is already capable of considering these factors, modifications were developed that allow simulation of base shaking. This approach allows consideration of this additional load source within a framework, the FAST code that is already familiar to many researchers and practitioners

Prediction of Groundwater Level Using Artificial Neural Networks Based on Efficient Input Variables Selection by Partial Mutual Information Algorithm

An accurate and reliable prediction of groundwater level in a region is very important for sustainable use and management of water resources. In this study, the generalized feedforward (GFF) and radial basis function (RBF) of artificial neural networks (ANNs) have been evaluated for monthly predicting groundwater levels in the Dezful-Andimeshk plain in southwestern Iran. The partial mutual information (PMI) algorithm was used to determine efficient input variables in ANNs. The results of using the PMI algorithm showed that efficient input variables for monthly predicting groundwater level for piezometers affected by water discharge and recharge include only water level in the current month. Also, efficient input variables for predicting the water level for piezometers affected only by water discharge include the water level in the current month, the water level in the previous month, the water level in the previous two months, transverse coordinates of piezometers to UTM, the water level in the previous three months, the water level in the previous four months, the water level in the previous five months and longitudinal coordinates of piezometers to UTM. In addition, efficient input variables of monthly predicting groundwater level for piezometers neither affected by water discharge nor water recharge, respectively, include the water level in the current month, the water level in the previous month, the water level in the previous two months, the water level in the previous three months, the water level in the previous four months, the water level in the previous five months, the water level in the previous six months, transverse coordinates of piezometer to UTM and longitudinal coordinates of piezometer to UTM. The results indicated that the GFF network is more accurate than the RBF network for monthly predicting groundwater level for piezometers including water discharge and recharge and piezometers including only water discharge. Also, the RBF network is more accurate for monthly predicting groundwater levels for piezometers that include neither water discharge nor recharge than the GFF network