3 resultados para Ortho-Neuron

em Digital Commons - Michigan Tech


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This study focuses on a specific engine, i.e., a dual-spool, separate-flow turbofan engine with an Interstage Turbine Burner (ITB). This conventional turbofan engine has been modified to include a secondary isobaric burner, i.e., ITB, in a transition duct between the high-pressure turbine and the low-pressure turbine. The preliminary design phase for this modified engine starts with the aerothermodynamics cycle analysis is consisting of parametric (i.e., on-design) and performance (i.e., off-design) cycle analyses. In parametric analysis, the modified engine performance parameters are evaluated and compared with baseline engine in terms of design limitation (maximum turbine inlet temperature), flight conditions (such as flight Mach condition, ambient temperature and pressure), and design choices (such as compressor pressure ratio, fan pressure ratio, fan bypass ratio etc.). A turbine cooling model is also included to account for the effect of cooling air on engine performance. The results from the on-design analysis confirmed the advantage of using ITB, i.e., higher specific thrust with small increases in thrust specific fuel consumption, less cooling air, and less NOx production, provided that the main burner exit temperature and ITB exit temperature are properly specified. It is also important to identify the critical ITB temperature, beyond which the ITB is turned off and has no advantage at all. With the encouraging results from parametric cycle analysis, a detailed performance cycle analysis of the identical engine is also conducted for steady-stateengine performance prediction. The results from off-design cycle analysis show that the ITB engine at full throttle setting has enhanced performance over baseline engine. Furthermore, ITB engine operating at partial throttle settings will exhibit higher thrust at lower specific fuel consumption and improved thermal efficiency over the baseline engine. A mission analysis is also presented to predict the fuel consumptions in certain mission phases. Excel macrocode, Visual Basic for Application, and Excel neuron cells are combined to facilitate Excel software to perform these cycle analyses. These user-friendly programs compute and plot the data sequentially without forcing users to open other types of post-processing programs.

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Presented here, is the work done with a series of binucleating ligands based on phosphine and phosphine oxide appended p-hydroquinones and their reactions towards various metals sources. The long term goal of the project was to produce coordination polymers that would have novel electronic, magnetic, and optical properties which would be of use in the field of molecular electronics. Binucleating ligands contained a p-hydroquinone motif in which various phosphine- and phosphine oxide substituents have been placed in the ortho position relative to each of the hydroxy position were synthesized. A previously published synthetic method for such lugands utilized n-BuLi to form a phenyl lithium intermediate which was quenched with chlorodiphenylphosphine. This technique was also used to produce a ligand with diisopropylphosphine groups. Phosphine ligands, containing the same structural motif, were also generated using LDA as the lithiating agent. This technique was found to be higher yielding. Phosphine chalcogenide ligands were accessed by further oxidizing the low valent phosphorous centers with either hydrogen peroxide or with elemental sulfur. These ligands were characterized using multinuclear NMR, low and high resolution mass spectroscopy, FTIR, and single crystal X-ray diffraction. Their electrochemical properties were explored with cyclic voltammetry. The phosphine appended ligands were used in the synthesis of a several bimetallic complexes. It was found that the ligands readily reacted with NiCp2 and NiCp*2, displacing one of the cyclopentadiene (Cp) or pentamethylcyclopentadiene (Cp*) rings. A cyclopentadiene complexes, containing diisopropylphine, was readily oxidized by[FeCp2]PF6 to give a NMR silent mixed valence complex. Cyclic voltammetry of these complexes showed a number of reversible waves with a large potential separation. The mixed valence compounds also showed a large absorbance band in the NIR region which was assigned to be an intervalence charge transfer. The cyclic voltammetry and NIR spectroscopy suggest that these systems are very capable of efficient metal-to-metal charge transfer. These complexes were characterized by multinuclear NMR, single crystal X-ray diffraction, UV/VIS-NIR spectroscopy and elemental analysis. The phosphine oxide ligands were reacted with a variety of different metal sources but limited success was gained in obtaining single crystals, allowing structural characterization of these compounds. Single crystals were obtained from products generated by reacting the diphenylphosphine oxide ligand with (Bipy)Cu(NO3)2 and Cu(NO3)2. In all cases the ligand had been further oxidized to a 2,5-dihydroxy-1,4-benzoquinone motif. In the reaction between the diphenylphosphine oxide ligand and (Bipy)Cu(NO3)2 it was found that the phosphine oxide moiety was involved with intermolecular coordination leading to the formation of a one-dimensional polymer composed of a series of bimetallic complexes tethered together. When NaSbF6 was present in the reaction with (Bipy)Cu(NO3)2 a unique tetrametallic complex was formed. Here the phospine oxide moiety was oriented so that two bimetallic complexes were bound together. If only Cu(NO3)2 was present, a two-dimensional polymeric sheet was formed where the ligand was present in two different coordination modes. The electronic properties of these complexes remained to be assessed.

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Neuromorphic computing has become an emerging field in wide range of applications. Its challenge lies in developing a brain-inspired architecture that can emulate human brain and can work for real time applications. In this report a flexible neural architecture is presented which consists of 128 X 128 SRAM crossbar memory and 128 spiking neurons. For Neuron, digital integrate and fire model is used. All components are designed in 45nm technology node. The core can be configured for certain Neuron parameters, Axon types and synapses states and are fully digitally implemented. Learning for this architecture is done offline. To train this circuit a well-known algorithm Restricted Boltzmann Machine (RBM) is used and linear classifiers are trained at the output of RBM. Finally, circuit was tested for handwritten digit recognition application. Future prospects for this architecture are also discussed.