2012 WSU Annual CGRS Abstracts

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    Use of artificial neural networks to detect damage in composite laminates
    (2012-02-16) Kral, Zachary Tyler; Horn, Walter J.; Steck, James E.
    Maintenance has remained an important issue in the aerospace structures and materials field. As technologies have improved, composites have begun to replace increasingly more structural components. However, these still have a long expected life for service use and damage can occur within that time. Ultrasonic sensors can be placed on or within composite laminates to scan for damage. Analysis of signals from these sensors is difficult for composites due to effects of material boundaries. A novel method of using artificial neural networks to interpret signals has been investigated for this research. A simple four sensor system was created for this study. Four sensors were placed 4.25 units apart. In a pitch-catch method, strain waves produced by one sensor (used as an actuator) passed through the material and were received by the other three sensors. The received waves are then analyzed by artificial neural networks and a damage position was predicted. This system has been trained to identify damage location within the square area for actuator signals ranging from 50 kHz to 100 kHz. The system of four sensors was demonstrated to predict the damage location with a confidence interval of 95%. The research presented is a novel method of interpreting ultrasonic signal analysis with artificial neural networks which could be adapted to future structural health monitoring systems.
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    New progress in self-healing technology of composite wind turbine blades
    (2012-02-16) Patlolla, Vamsidhar Reddy; Asmatulu, Ramazan
    Wind turbine blades are subjected to external cyclic loadings, resulting in the development of micro and nanocracks, which in course of time becomes macro cracks, thus leading to fatigue and failure. The concept of self-healing composite materials might be introduced into the blade manufacturing to reduce the cost and to increase the life expectancy of the turbine blades. This can be performed by introducing urea-formaldehyde (UF) micro capsules into the epoxy matrix of the composite materials. The urea-formaldehyde microcapsules are filled with dicyclopentadiene (DCPD) which acts as the healing agent. When DCPD is introduced into the crack of the epoxy matrix, it reacts with a catalyst in the matrix and heals the cracks. The dispersion of nanoscale inclusions in the epoxy matrix has the potential of increasing the mechanical properties of the polymer composite in a great deal. When the nanoscale inclusions are used as reinforcements in the composite material, the rate of crack growth could be considerably reduced. This work deals with the self-healing of the wind turbine rotor blades. We used different nanoscale inclusions in the microspheres of DCPD to increase the healed fracture toughness and avoid crack regrowth. Wind farms in Kansas are producing 1228 MW of energy and the new wind farms being constructed would produce 921 MW of energy making it one of the biggest industries in the region. This research potentially increases the service life of the composite wind blades and reduces the overall costs. This concept can also be used for the repair of wind turbine rotor blades which helps creating many new jobs in this sector.
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    Impact on CO2 emission due to Electric Vehicle charging and distributed wind generation
    (2012-02-16) Argade, Sachin; Aravinthan, Visvakumar; Jewell, Ward T.
    To reduce the greenhouse gas emission and fossil fuel dependency, electric vehicles (EV) are becoming viable options. EV charging is seen as an extra load on the electric power distribution system, and if not properly coordinated, it could increase the greenhouse gas emission from the electric power generators. This extra burden can be relieved by use of renewable distributed generation. By introducing distributed generation to support EV charging loading on traditional generation will be reduced and hence reducing the CO2 emissions. Type of distribution generation will result in variable CO2 emission levels due to their limited availability. This study focuses on the impact of wind generation on electric vehicle charging. There are three levels of electric vehicle (EV) charging. Level-1 is slow AC charging, level-2 is fast AC charging and level-3 is fast DC charging. This work analyzes the AC charging (both level-1 and level-2) and its effect on overall CO2 emissions of traditional generation with the presence of distribution generation.
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    The effects of texting and driving on hazard perception and the adoption of driver response strategies
    (2012-02-16) Burge, Rondell J.; Chaparro, Alex
    Hazard perception has received little attention compared to measures of vehicular control in studies exploring the effects of texting on driving performance, despite being a more direct measure of crash risk. Twenty participants (10 male; 10 female) were recruited to drive in a simulator, specifically designed to measure situational awareness, while text messaging in order to assess hazard detection performance. Two text message conditions were used to compare interference at early vs. late stages of information processing. Signal Detection Theory (SDT) analysis revealed the adoption of a more liberal response criterion (B”) (i.e. increased false alarms and decreased misses) when text messaging interfered at an early stage but not at a late stage of processing (F(2,38) = 3.76, p < .05). Furthermore, reaction time and text errors increased when interference occurred at a later stage (F(2,38) = 29.90, p < .001 and F(1,19) = 5.869, p < .05, respectively). These findings suggest that the impact of text messaging on the detection of driving hazard depends in part, on the stage (late vs. early) of information processing, particularly in the adoption of response strategies.
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    Improving reading: understanding the role of working memory and ease of inference generation
    (2012-02-16) Mueller, Melinda K.; Bohn-Gettler, Catherine M.
    Reading scores from the National Assessment of Educational Progress showed 24% of Kansas’ fourth grade public school students were below basic reading levels. This shows there is a significant need for researchers to understand the different strategies readers use to successfully comprehend a text. An important skill for understanding texts is being able to fill in missing information from the text; this is called a bridging inference. When there is a coherence break in a text, readers will generate bridging inferences, but this can cause processing delays. This study examined whether the ease which with the bridging inferences could be generated is related to how difficult it is to generate the inference. Thus, participants in the current experiment read several passages in which the ease of generating an inference was gradually decreased by manipulating the explicitness of an action. Reading times for target sentences that followed the critical sentences increased as explicitness decreased. In addition, reader skills, such as working memory capacity, are related to inference generation skills. The effect of explicitness was more pronounced in readers with high working memory. This provides preliminary evidence that processing delays during bridging inference generation are affected by ease of generation. Understanding the types of processing, such as bridging inferences, that can improve comprehension among readers of all ages is critical for helping struggling readers in Kansas succeed.