992 resultados para multiloop amplitude
Resumo:
In 1957, the Iowa State Highway Commission, with financial assistance from the aluminum industry, constructed a 220-ft (67-m) long, four-span continuous, aluminum girder bridge to carry traffic on Clive Road (86th Street) over Interstate 80 near Des Moines, Iowa. The bridge had four, welded I-shape girders that were fabricated in pairs with welded diaphragms between an exterior and an interior girder. The interior diaphragms between the girder pairs were bolted to girder brackets. A composite, reinforced concrete deck served as the roadway surface. The bridge, which had performed successfully for about 35 years of service, was removed in the fall of 1993 to make way for an interchange at the same location. Prior to the bridge demolition, load tests were conducted to monitor girder and diaphragm bending strains and deflections in the northern end span. Fatigue testing of the aluminum girders that were removed from the end spans were conducted by applying constant-amplitude, cyclic loads. These tests established the fatigue strength of an existing, welded, flange-splice detail and added, welded, flange-cover plates and horizontal web plate attachment details. This part, Part 2, of the final report focuses on the fatigue tests of the aluminum girder sections that were removed from the bridge and on the analysis of the experimental data to establish the fatigue strength of full-size specimens. Seventeen fatigue fractures that were classified as Category E weld details developed in the seven girder test specimens. Linear regression analyses of the fatigue test results established both nominal and experimental stress-range versus load cycle relationships (SN curves) for the fatigue strength of fillet-welded connections. The nominal strength SN curve obtained by this research essentially matched the SN curve for Category E aluminum weldments given in the AASHTO LRFD specifications. All of the Category E fatigue fractures that developed in the girder test specimens satisfied the allowable SN relationship specified by the fatigue provisions of the Aluminum Association. The lower-bound strength line that was set at two standard deviations below the least squares regression line through the fatigue fracture data points related well with the Aluminum Association SN curve. The results from the experimental tests of this research have provided additional information regarding behavioral characteristics of full-size, aluminum members and have confirmed that aluminum has the strength properties needed for highway bridge girders.
Resumo:
Energy demand is an important constraint on neural signaling. Several methods have been proposed to assess the energy budget of the brain based on a bottom-up approach in which the energy demand of individual biophysical processes are first estimated independently and then summed up to compute the brain's total energy budget. Here, we address this question using a novel approach that makes use of published datasets that reported average cerebral glucose and oxygen utilization in humans and rodents during different activation states. Our approach allows us (1) to decipher neuron-glia compartmentalization in energy metabolism and (2) to compute a precise state-dependent energy budget for the brain. Under the assumption that the fraction of energy used for signaling is proportional to the cycling of neurotransmitters, we find that in the activated state, most of the energy ( approximately 80%) is oxidatively produced and consumed by neurons to support neuron-to-neuron signaling. Glial cells, while only contributing for a small fraction to energy production ( approximately 6%), actually take up a significant fraction of glucose (50% or more) from the blood and provide neurons with glucose-derived energy substrates. Our results suggest that glycolysis occurs for a significant part in astrocytes whereas most of the oxygen is utilized in neurons. As a consequence, a transfer of glucose-derived metabolites from glial cells to neurons has to take place. Furthermore, we find that the amplitude of this transfer is correlated to (1) the activity level of the brain; the larger the activity, the more metabolites are shuttled from glia to neurons and (2) the oxidative activity in astrocytes; with higher glial pyruvate metabolism, less metabolites are shuttled from glia to neurons. While some of the details of a bottom-up biophysical approach have to be simplified, our method allows for a straightforward assessment of the brain's energy budget from macroscopic measurements with minimal underlying assumptions.