<p dir="ltr">Transportation and energy production industries strongly rely on improvements in gas turbine performance. The quantification of these improvements is dependent on the accuracy of the measurements performed during testing. An increase of 0.5\% in efficiency is sufficient to secure a new development program worth millions of dollars, but in the case of temperature measurements, uncertainties below 0.5 K are required, which presents a challenge. This work selects heat flux estimation and total temperature measurement uncertainties as major contributors for efficiency uncertainty.</p><ul><li>Heat flux measurements are critical to evaluate the impact on the efficiency. Additionally, thermal fatigue in turbine airfoils defines the life cycle of the engine core. This work performs an estimation of the heat transfer via a simplified numerical model that uses infrared (IR) measurements in the surface of the casing to predict the temperature of the passage wall. The model is validated with real cool-down data of the turbine to yield results within a 10\% of the actual temperature.</li><li>Total temperature measurement suffers from errors due to heat transfer effects in the probe. Two dominant sources of errors are convection and conduction between the thermocouple wires, the probe support, and the flow. These effects can be treated in two different categories: the velocity error, created by a non-isentropic reduction of the flow velocity upstream the thermocouple junction, and the thermal equilibrium effects between the junction and the probe support, involving heat transfer through the wire to the shield and the probe stem due to temperature differences between each component (the so-called \emph{conduction error}). An open jet stand is used to evaluate the effects of velocity error at various Mach numbers. The conduction error is addressed with the design and manufacturing of dual-wire thermocouple probes. The readings from two wires with different length-to-diameter ratios are used to correct for the flow total temperature. This probe yielded a recovery factor of 0.99 +/- 0.01 at Mach 0.6.</li></ul><p></p>

Dendritic spines are fundamental units of information processing within the nervous system, responsible for independent modulation of synaptic input to neurons. Filopodia, often morphologically indistinguishable from spines, are involved in formation of synapses during neuronal development. Despite the importance of these structures for neuronal function, no detailed study of their presence on motoneurons has yet been made. Here, the presence of spines on hypoglossal motoneurons (HMs) is described at three developmental stages: at P0–2 and P9–11, spines are present at an average density of ~0.1 spines/micron, but at P19 spine density becomes negligible. In P0–2 and P9–11, spines are nonuniformly distributed, occuring in clusters, and at lower density in the most proximal and distal regions to the soma than at intermediate regions. HM spines coincide with a decrease in cell input resistance, which reduces excitability during development. Thus one may speculate that these spines are involved in the formation of new synapses required to maintain adequate excitatory drive. A major difficulty for the study of spines is their small size, which complicates measurement using optical methods. Here, I present a novel method for reconstructing spine morphology using geometric models based on a priori knowledge of spine structure. Tests of the technique using simulated data indicate that it has a resolving capability of up to 40 nm (limited by noise). The technique has been used to measure dendritic spines on HMs, showing that these structures have necks as small as 0.22 micron. For purely passive modulation of synaptic strength, spine necks need to be <~ 0.15 micron. These data suggest that if modulation of synaptic input occurs, biochemical and/or active electrical processes are needed. The methods developed in this Thesis, which have here been applied to HMs, are generally applicable to the study of spine morphology, and its effect on synaptic processing, in all classes of neurons.

Dendritic spines are fundamental units of information processing within the nervous system, responsible for independent modulation of synaptic input to neurons. Filopodia, often morphologically indistinguishable from spines, are involved in formation of synapses during neuronal development. Despite the importance of these structures for neuronal function, no detailed study of their presence on motoneurons has yet been made. Here, the presence of spines on hypoglossal motoneurons (HMs) is described at three developmental stages: at P0–2 and P9–11, spines are present at an average density of ~0.1 spines/micron, but at P19 spine density becomes negligible. In P0–2 and P9–11, spines are nonuniformly distributed, occuring in clusters, and at lower density in the most proximal and distal regions to the soma than at intermediate regions. HM spines coincide with a decrease in cell input resistance, which reduces excitability during development. Thus one may speculate that these spines are involved in the formation of new synapses required to maintain adequate excitatory drive. A major difficulty for the study of spines is their small size, which complicates measurement using optical methods. Here, I present a novel method for reconstructing spine morphology using geometric models based on a priori knowledge of spine structure. Tests of the technique using simulated data indicate that it has a resolving capability of up to 40 nm (limited by noise). The technique has been used to measure dendritic spines on HMs, showing that these structures have necks as small as 0.22 micron. For purely passive modulation of synaptic strength, spine necks need to be <~ 0.15 micron. These data suggest that if modulation of synaptic input occurs, biochemical and/or active electrical processes are needed. The methods developed in this Thesis, which have here been applied to HMs, are generally applicable to the study of spine morphology, and its effect on synaptic processing, in all classes of neurons.

Dendritic spines are fundamental units of information processing within the nervous system, responsible for independent modulation of synaptic input to neurons. Filopodia, often morphologically indistinguishable from spines, are involved in formation of synapses during neuronal development. Despite the importance of these structures for neuronal function, no detailed study of their presence on motoneurons has yet been made. Here, the presence of spines on hypoglossal motoneurons (HMs) is described at three developmental stages: at P0–2 and P9–11, spines are present at an average density of ~0.1 spines/micron, but at P19 spine density becomes negligible. In P0–2 and P9–11, spines are nonuniformly distributed, occuring in clusters, and at lower density in the most proximal and distal regions to the soma than at intermediate regions. HM spines coincide with a decrease in cell input resistance, which reduces excitability during development. Thus one may speculate that these spines are involved in the formation of new synapses required to maintain adequate excitatory drive. A major difficulty for the study of spines is their small size, which complicates measurement using optical methods. Here, I present a novel method for reconstructing spine morphology using geometric models based on a priori knowledge of spine structure. Tests of the technique using simulated data indicate that it has a resolving capability of up to 40 nm (limited by noise). The technique has been used to measure dendritic spines on HMs, showing that these structures have necks as small as 0.22 micron. For purely passive modulation of synaptic strength, spine necks need to be <~ 0.15 micron. These data suggest that if modulation of synaptic input occurs, biochemical and/or active electrical processes are needed. The methods developed in this Thesis, which have here been applied to HMs, are generally applicable to the study of spine morphology, and its effect on synaptic processing, in all classes of neurons.

Dendritic spines are fundamental units of information processing within the nervous system, responsible for independent modulation of synaptic input to neurons. Filopodia, often morphologically indistinguishable from spines, are involved in formation of synapses during neuronal development. Despite the importance of these structures for neuronal function, no detailed study of their presence on motoneurons has yet been made. Here, the presence of spines on hypoglossal motoneurons (HMs) is described at three developmental stages: at P0–2 and P9–11, spines are present at an average density of ~0.1 spines/micron, but at P19 spine density becomes negligible. In P0–2 and P9–11, spines are nonuniformly distributed, occuring in clusters, and at lower density in the most proximal and distal regions to the soma than at intermediate regions. HM spines coincide with a decrease in cell input resistance, which reduces excitability during development. Thus one may speculate that these spines are involved in the formation of new synapses required to maintain adequate excitatory drive. A major difficulty for the study of spines is their small size, which complicates measurement using optical methods. Here, I present a novel method for reconstructing spine morphology using geometric models based on a priori knowledge of spine structure. Tests of the technique using simulated data indicate that it has a resolving capability of up to 40 nm (limited by noise). The technique has been used to measure dendritic spines on HMs, showing that these structures have necks as small as 0.22 micron. For purely passive modulation of synaptic strength, spine necks need to be <~ 0.15 micron. These data suggest that if modulation of synaptic input occurs, biochemical and/or active electrical processes are needed. The methods developed in this Thesis, which have here been applied to HMs, are generally applicable to the study of spine morphology, and its effect on synaptic processing, in all classes of neurons.

<p>Recently, the use of cosmic ray muons in critical national security applications, e.g., nuclear nonproliferation and safeguards verification, has gained attention due to unique muon properties such as high energy and low attenuation even in very dense materials. Applications where muon tomography has been demonstrated include cargo screening for detection of special nuclear materials smuggling, source localization, material identification, determination of nuclear fuel debris location in nuclear reactors, etc. However, muon image reconstruction techniques are still limited in resolution mostly due to multiple Coulombscattering (MCS) within the target object. Improving and expanding muon tomography would require development of efficient & flexible physics-based algorithms to model the MCS process and accurately estimate the most probable trajectory of a muon as it traverses the target object. The present study introduces a novel algorithmic approach that utilizes Bayesian probability theory and a Gaussian approximation of MCS to estimate the most probable path of cosmic ray muons as they traverse uniform media.</p> <p>Using GEANT4, an investigation was conducted involving the trajectory of 10,000 muon particles that underwent bombardment from a point source parallel to the x-axis. The proposed algorithm was assessed through four types of simulations. In the first type, muons with energies of 1 GeV, 3 GeV, 10 GeV, and 100 GeV were utilized to evaluate the algorithms’ performance and accuracy. The second type of simulation involved the use of target cubes composed of different materials, including aluminum, iron, lead, and uranium. These simulations specifically focused on muons with an energy of 3 GeV. Next, the third type of simulation entailed employing target cubes with varying lengths, such as 10 cm, 20 cm, 40 cm, and 80 cm, specifically using muons with an energy of 3 GeV and a uranium target. Lastly, all the previous simulations were revised to accommodate a source of poly-energetic muons. This revision was undertaken to create a more realistic source scenario that aligns with the distribution of muon energies encountered in real-world situations.</p> <p>The results demonstrate significant improvements in precision and muon flux utilization when comparing different algorithms. The Generalized Muon Trajectory Estimation (GMTE) algorithm shows around 50% improvement in precision compared to currently used Straight Line Path (SLP) algorithm across all test scenarios. Additionally, GMTE algorithm exhibits around 38% improvement in precision compared to the extensively used Point of Closest Approach (PoCA) algorithm. Similarly for both mono and poly energetic source of muons, the GMTE algorithm shows 10%-35% increase in muon flux utilization for high Z materials and a 10%-15% increase for medium Z materials compared to the PoCA algorithm. Similarly, it demonstrates 6%-9% increase in muon flux utilization for both medium and high Z materials compared to the SLP algorithm across all test scenarios. These results highlight the enhanced performance and efficiency of GMTE algorithm in comparison to SLP and PoCA algorithms.</p> <p>Through these extensive simulations, our objective was to comprehensively evaluate the performance and effectiveness of the proposed algorithm across a range of variables, including energy levels, materials, and target geometries. The findings of our study demonstrate that the utilization of these algorithm enables improved resolution and reduced measurement time for cosmic ray muons when compared with current SLP and PoCA algorithm.</p>