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Ellen: Well, that's an easy one! The short answer is my awesome running community. I'm truly blessed to have found a fabulous group of friends who are willing to wake up extra-early and run with me on any and all days of the week. Before I knew them I ran, and I even ran marathons, but not nearly as consistently or as fast as do now. And I am certainly having more fun now than I was back in my pre-run group days! It's really hard for me to imagine how I ever ran without this group, and I'm so appreciative of how they inspire me, motivate me, teach me important stuff about life and myself, and crack me up daily!
Furthermore, I cannot say that the Hansons method doesn't work. As much as I disliked doing the workouts, there is no denying that I did see fitness gains from doing them and I was executing my MGP tempo runs like a boss by the time I decided to pull the plug on the marathon. It is probably also worth mentioning that I ran a PR and cracked a milestone time barrier that had previously eluded me in the half marathon that I ended up running as a NYC Marathon replacement.
Your arms should swing at a 90 degree angle at your sides, and they should not swing across your chest. Try not to swing your arms across the center of your body. Your hands shouldn't be balled up into tightened fists. Keep your hands and fingers loose, as if you were carrying eggshells between your thumbs and index fingers, and you did not want them to crack any further. Why should you imagine you're a weirdo that's running around with cracked eggs in your hands? At this point we don't have a good answer to that question. :)
Pete, do you still recommend sporttracks now that it is paid software? ($35) Have you checked out how Garmin Connect compares to Sporttracks? I just got a 305 and am wondering if Sporttracks is still a value given the price and possible feature enhancements to other options
I have a 2010 Ford explorer sport Trac unlimited and engine light is on and it is overheating I took to AutoZone to have it tested what popped up was coolant leak could it be because it could maybe have a cracked head or blown head gasket plz someone help me I am disable
Energy is released within a material for two different transition events when the deformation of the material changes from pure elastic deformation to a combination of elastic and plastic deformation and at the point of crack extension associated with fracture. This energy is detectable by the piezoelectric sensors of the acoustic emission system. The amount of energy released by a fracture is generally far greater than the amount accompanying plastic deformation. However, both instances occur for growing cracks. The tip of a crack is the site for very large stresses. Before the crack extends, a region or zone of plastic deformation is achieved in the vicinity of the crack tip. This plastic region can be approximated, using Von Mises criterion, to determine the boundaries of the plastic zone. For the thin-walled structures of this research, the plastic zone covered a very small region near the crack tip, while the major portion of the structure underwent purely elastic deformation.
As a crack is initiated in the material, the plastic zone at the tip is quickly formed. As loading to the structure is increased, the crack will increase in size as illustrated in Figure 2(a). Thus at any increment of crack propagation a crescent-shaped region of new plastic deformation is created as illustrated in Figure 2(b). This shape may vary for fatigue loading, but for simplicity a basic shape can be examined.
Borrowing an idea from the distributed point source method [8] for approximating wave sources in a material, consider that each molecular change is a point source of infinitesimally small diameter, which releases a strain wave into the surrounding area. These point sources could be placed close together, forming a wave front with a specific geometric shape. By the superposition principle overlapping waves will start to cancel one another as the distance between the point sources becomes smaller. As the number of point sources increases to infinity and the distance between points approaches zero, the geometric shape of the wave becomes continuous and smooth. Waves will travel outward with this smooth shape in a direction normal to the boundary of the shape. This idea is illustrated in Figure 3, using a straight line as an example. This idea was originally used for generating wave shapes by piezoelectric actuators. However, this idea may also be applied to a collection of point sources generated by the crescent shape of the new plastic region formed during crack growth rather than a series of actuators. The wider region of the crescent shape, near the horizontal axis in Figure 2(b), contains more energy than at the sharp, pointed tips of the new plastic zone. Thus acoustic emission sensors ahead of the tip of the growing crack will detect strain waves of higher magnitude of energy when compared to sensors detecting the same wave above or behind the direction of a growing crack (see Figure 4). For example, in the figure, energy from a strain wave received by sensor (b) would be greater than the energy of the same wave received by sensor (a). Based on the direction of the growing crack, a wedge shape of intensity or magnitude of energy can be drawn, protruding outward from the crack tip. In other words, the detected wave energy increases as approaches 0. This allows for a line-of-sight principle to be applied to triangulation methods to compare detections at multiple sensors resulting from the same wave. This effect is observed in a following experiment using aluminum material to confirm the notion of directional strain waves propagating from a crack tip during crack extension.
The nervous system of humans consists of a network of passive sensors capable of detecting changes within the body. If a change is detected, the system reacts by sending a signal to the brain for further analysis of the situation. More intense signals are generated for larger anomalies that identify the specific location of the anomaly. A similar idea for a passively scanning SHM system for an aircraft has been studied for this paper. That is, as a crack grows in a structural component, the amount of energy released as strain waves is linked to the size of the crack propagation. For large crack growth, more energy is released, and thus more intense strain waves are detected by an acoustic emission system.
The first series of experiments focused on determining the extension of a crack over a short period of time using an acoustic emission system. In the case of stable crack growth, further extension will cease after a specific crack length is obtained. The crack will not extend further until a certain load condition is applied. These small crack extensions consist of rapid increasing bursts that are close to instantaneous. The purpose of these experiments was to use the detections of an acoustic emission system for a known crack extension to train an artificial neural network to link certain detections to specific crack length growths. The trained ANN could later be used to determine the length of a crack from acoustic emission measurements.
Figure 6 contains drawings that detail the dimensions of the two different test panels used in the experiments. The panel, shown in Figure 6(a), was subjected to a uniaxial tensile load to initiate crack extension in order to measure the magnitude of an increment of crack growth. An initial crack was cut into the panel from one of the side edges in the test region, and then the panel was statically loaded with an MTS Sintech 5/G machine through a pin and clevis setup as illustrated in Figure 7. The loading was gradually increased, until crack extension occurred. The crack length was measured at specific load intervals by an observer, using digital calipers. These measured crack lengths were used to create a learning dataset for an artificial neural network. Likewise, they were used to compare the crack extension calculated with a neural network relative to the actual measured values. The acoustic emission sensors, located as shown in Figure 7, continuously monitored for any crack growth during the increasing-load process. The recorded acoustic emission signals were later used for analysis with an artificial neural network. Only two sensors were used for this test since crack growth size was desired and not the position of the crack (see Figure 7(b)). The sensors were placed at similar positions away from the crack tip to avoid any effects of plastic zone deformation as well as confirm that the sensors were functioning properly.
The strain wave data were received continuously over time. For an artificial neural network input dataset, a small time interval was used for determining the increment of crack growth associated with the large number of strain waves detected over the short period of time of the crack extension. The energy values from each detected wave were placed into a 10 bin histogram. The output consisted of the change in crack growth or difference from initial size to final size over that time step. These input and output datasets were used in the network architecture, illustrated in Figure 8. Experimenting with different network architectures, two hidden layers were found to increase precision and accuracy of the output values, while minimizing the processing time of the network. This neural network system proved to work well for predicting the magnitude of crack growth for a flat panel.
The next series of experiments focused on crack location and the effects of sensor placement, relative to a crack tip position. The purpose of the experiment was to develop an artificial neural network system, which could relate to the detections acquired from nearby acoustic emission sensors and determine the location of the crack tip from which the strain waves originated. Although more research will be required to develop an artificial neural network capable of this process, the research described in this paper concerns the theoretical aspects. Through a simple experiment the relative detections of neighboring acoustic emission sensors were compared by an observer. Based upon existing trends a neural network could be developed and trained to find similar trends. The inputs consisted of the same detected wave over several acoustic emission sensors; in this case, the maximum amplitude of each detected wave was the characteristic used. The output of the artificial neural network was the position of the crack tip from which the wave originated. 2b1af7f3a8