Resistance-foil strain-gage technology as applied to composite materials
A general review of existing strain-gage technologies as applied to orthotropic-composite materials is given. The specific topics addressed are gage-bonding procedures, transverse-sensitivity effects, errors due to gage misalignment, and temperature-compensation methods. The discussion is supplemented by numerical examples where appropriate. It is shown that the orthotropic behavior of composites can result in experimental error which would not be expected based on practical experience with isotropic materials. In certain cases, the transverse sensitivity of strain gages and/or slight gage misalignment can result in strain-measurement errors exceeding 50 percent.
A biorobotic model of the sunfish pectoral fin for investigations of fin sensorimotor control
A comprehensive understanding of the control of flexible fins is fundamental to engineering underwater vehicles that perform like fish, since it is the fins that produce forces which control the fish's motion. However, little is known about the fin's sensory system or about how fish use sensory information to modulate the fin and to control propulsive forces. As part of a research program that involves neuromechanical and behavioral studies of the sunfish pectoral fin, a biorobotic model of the pectoral fin and of the fin's sensorimotor system was developed and used to investigate relationships between sensory information, fin ray motions and propulsive forces. This robotic fin is able to generate the motions and forces of the biological fin during steady swimming and turn maneuvers, and is instrumented with a relatively small set of sensors that represent the biological lateral line and receptors hypothesized to exist intrinsic to the pectoral fin. Results support the idea that fin ray curvature, and the pressure in the flow along the wall that represents the fish body, capture time-varying characteristics of the magnitude and direction of the force created throughout a fin beat. However, none of the sensor modalities alone are sufficient to predict the propulsive force. Knowledge of the time-varying force vector with sufficient detail for the closed-loop control of fin ray motion will result from the integration of characteristics of many sensor modalities.
Applicability of the International Roughness Index as a Predictor of Asphalt Pavement Condition
This note establishes the relationship between the surface distress of an asphalt pavement and its roughness, as conveyed respectively by the pavement condition index (PCI) and the international roughness index (IRI). The DataPave software provides the roughness of varied roadway pavement sections from the North Atlantic region that were investigated under the long term pavement performance (LTPP) study. The MicroPAVER1 software system computes the condition of the same sections using cross-referenced distress data from DataPave. A transformed linear regression model predicts pavement condition given roughness. It confirms the acceptability of the IRI as a, albeit not the sole, predictor variable of the PCI whereby the former accounts for the majority, close to 59%, of the variations in the latter. Further, an analysis of variance confirms the existence of a strong relationship between both variables.