Paper | Title | Page |
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TUPHA065 | Recent Enhancements to the Los Alamos Isotope Production Facility | 548 |
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Funding: The work described was funded by the U.S. Department of Energy, Office of Science via the Isotope Development and Production for Research and Applications subprogram in the Office of Nuclear Physics. Isotopes produced at Los Alamos National Laboratory (LANL) are saving lives, advancing cutting-edge research, and helping to address national security questions. For the past two years LANL's Accelerator Operations & Technology Division has executed a $6.4M improvement project for the Isotope Production Facility. The goals are to reduce the programmatic risk and enhance facility reliability while at the same time pursuing opportunities to increase general isotope production capacity. This has led to some exciting innovations. In this paper we will discuss the engineering designs for our new collimator, which is both adjustable and 'active' (i.e. equipped with beam current and temperature measurements), as well as our upgraded beam raster system and new beam diagnostics capabilities. We will also report on results obtained and lessons learned from the commissioning phase and initial production run. LA-UR-17-22778 |
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Poster TUPHA065 [0.755 MB] | |
DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2017-TUPHA065 | |
Export • | reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml) | |
THPHA128 | Applications of Kalman State Estimation in Current Monitor Diagnostic Systems | 1673 |
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Funding: Work supported by US Department of Energy under contract DE-AC52-06NA25396. Traditionally, designers of transformer-based beam current monitor diagnostic systems are constrained by fundamental trade-offs when reducing distortion in time-domain beam-pulse facsimile waveforms while also attempting to preserve information in the frequency-domain. When modelling the sensor system with a net-work of linear time-invariant passive components, and a state-based representation based on first-order differential equations, we identify two internal dynamical states isolated from each other by the parasitic resistance in the transformer windings. They are the parasitic capacitance voltage across the transformer's windings, and the transformer inductor current. These states are typically imperfectly observed due to noise, component value variance, and sensor component network topology. We will discuss how feedback-based Kalman State Estimation implemented within digital signal-processing might be employed to reduce negative impacts of noise along with component variance, and how Kalman Estimation might also optimize the conflicting goals of beam-pulse facsimile waveform fidelity together with preservation of fre-quency domain information. |
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Poster THPHA128 [1.757 MB] | |
DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2017-THPHA128 | |
Export • | reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml) | |