This thesis takes an initial step in the research on the long term behaviour of composite-to-metal bonds that are loaded under hygrothermal conditions. It focuses on the response and behaviour of the adhesive in these bonds under moisture and loading. The thesis is inconclusive on the effect of loading on the water uptake. The load level and environment were however found to have a significant influence on the creep behaviour and residual strength of the bonds. It was suggested that at the initial stages of water absorption creep is suppressed while at later stages with higher moisture percentages creep is promoted. Low load levels had a positive effect on the residual lap shear strength and stiffness. For higher load levels and immersion in water the residual lap shear strength and stiffness are decreased. The experimental work also suggests that Araldite 2015 might be affected by ambient humidity.

Mathematical models with varying degrees of complexity have been proposed and simulated in an attempt to represent the intricate mechanisms of the human neuron. One of the most biochemically realistic and analytical models, based on the Hodgkin–Huxley (HH) model, has been selected for study in this paper. In order to satisfy the model's computational demands, we present a simulator implemented on Intel Xeon Phi Knights Landing manycore processors. This high-performance platform features an x86-based architecture, allowing our implementation to be portable to other common manycore processing machines. This is reinforced by the fact that Phi adopts the popular OpenMP and MPI programming models. The simulator performance is evaluated when calculating neuronal networks of varying sizes, density and network connectivity maps. The evaluation leads to an analysis of the neuronal synaptic patterns and their impact on performance when tackling this type of workload on a multinode system. It will be shown that the simulator can calculate 100 ms of simulated brain activity for up to 2 millions of biophysically-accurate neurons and 2 billion neuronal synapses within one minute of execution time. This level of performance renders the application an efficient solution for large-scale detailed model simulation.@en

Mathematical models with varying degrees of complexity have been proposed and simulated in an attempt to represent the intricate mechanisms of the human neuron. One of the most biochemically realistic and analytical models, based on the Hodgkin–Huxley (HH) model, has been selected for study in this paper. In order to satisfy the model's computational demands, we present a simulator implemented on Intel Xeon Phi Knights Landing manycore processors. This high-performance platform features an x86-based architecture, allowing our implementation to be portable to other common manycore processing machines. This is reinforced by the fact that Phi adopts the popular OpenMP and MPI programming models. The simulator performance is evaluated when calculating neuronal networks of varying sizes, density and network connectivity maps. The evaluation leads to an analysis of the neuronal synaptic patterns and their impact on performance when tackling this type of workload on a multinode system. It will be shown that the simulator can calculate 100 ms of simulated brain activity for up to 2 millions of biophysically-accurate neurons and 2 billion neuronal synapses within one minute of execution time. This level of performance renders the application an efficient solution for large-scale detailed model simulation.@en