DEVELOPMENT OF AN OFF-LINE RAINFLOW COUNTING ALGORITHM WITH TEMPORAL PARAMETERS AND DATA REDUCTION

Rainflow counting methods convert a complex load time history into a set of load reversals for use in fatigue damage modeling. Rainflow counting methods were originally developed to assess fatigue damage associated with mechanical cycling where creep of the material under load was not considered to be a significant contributor to failure. However, creep is a significant factor in some cyclic loading cases such as solder interconnects under temperature cycling. In this case, fatigue life models require the dwell time to account for stress relaxation and creep. This study develops a new version of the multi-parameter rainflow counting algorithm that provides a range-based dwell time estimation for use with time-dependent fatigue damage models. To show the applicability, the method is used to calculate the life of solder joints under a complex thermal cycling regime and is verified by experimental testing. An additional algorithm is developed in this study to provide data reduction in the results of the rainflow counting. This algorithm uses a damage model and a statistical test to determine which of the resultant cycles are statistically insignificant to a given confidence level. This makes the resulting data file to be smaller, and for a simplified load history to be reconstructed.

The Impact of Motor Learning on Motor Behavior and Cortical Dynamics in a Complex Stressful Social Environment

An economy of effort is a core characteristic of highly skilled motor performance often described as being effortless or automatic. Electroencephalographic (EEG) evaluation of cortical activity in elite performers has consistently revealed a reduction in extraneous associative cortical activity and an enhancement of task-relevant cortical processes. However, this has only been demonstrated under what are essentially practice-like conditions. Recently it has been shown that cerebral cortical activity becomes less efficient when performance occurs in a stressful, complex social environment. This dissertation examines the impact of motor skill training or practice on the EEG cortical dynamics that underlie performance in a stressful, complex social environment. Sixteen ROTC cadets participated in head-to-head pistol shooting competitions before and after completing nine sessions of skill training over three weeks. Spectral power increased in the theta frequency band and decreased in the low alpha frequency band after skill training. EEG Coherence increased in the left frontal region and decreased in the left temporal region after the practice intervention. These suggest a refinement of cerebral cortical dynamics with a reduction of task extraneous processing in the left frontal region and an enhancement of task related processing in the left temporal region consistent with the skill level reached by participants. Partitioning performance into ‘best’ and ‘worst’ based on shot score revealed that deliberate practice appears to optimize cerebral cortical activity of ‘best’ performances which are accompanied by a reduction in task-specific processes reflected by increased high-alpha power, while ‘worst’ performances are characterized by an inappropriate reduction in task-specific processing resulting in a loss of focus reflected by higher high-alpha power after training when compared to ‘best’ performances. Together, these studies demonstrate the power of experience afforded by practice, as a controllable factor, to promote resilience of cerebral cortical efficiency in complex environments.