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This poster outlines several projects conducted on behalf of the construction EHS&Q staff at INL. Among the projects outlined is noise dosimetry conducted on numerous people working with the construction projects, chemical inventories gathered from several construction subcontractors, and secondary chemical container label creation.
High-Q superconducting resonators have been proposed and developed as detectors of light-by-light scattering mediated by the hypothesized axion or virtual electron–positron pairs in quantum electrodynamics: the Euler–Heisenberg (EH) interaction. Photon frequency and mode conversion is central to the scheme for detecting such rare events. Superconducting resonators are nonlinear devices. The Meissner screening currents that confine the electromagnetic fields to the vacuum region of a superconducting RF cavity are nonlinear functions of the EM field at the vacuum–superconducting interface, and as a result can generate source currents and frequency conversion of microwave photons in the cavity. In this report we consider photon frequency and mode conversion in superconducting resonators with high quality factors from Meissner currents in single- and dual-cavity setups proposed for axion and QED searches based on light-by-light scattering. In a single cavity with two pump modes, photon frequency conversion by the Meissner screening current dominates photon generation by the EH interaction for cavities with Q≲1012. The Meissner currents also generate background photons that limit the operation of the resonator for axion detection in three-mode, single-cavity setups. We also consider the leakage of photons from pump modes into the signal mode for both axion- and EH-mediated light-by-light scattering. Photon frequency conversion by the EH interaction can compete with Meissner and leakage radiation in ultra-high-Q cavities that are beyond the current state of the art. Meissner radiation and leakage backgrounds can be suppressed in dual-cavity setups with appropriate choices for pump and spectator modes, as well as the single-cavity setup proposed for heterodyne detection of galactic axion dark matter.
Bastnaesite, monazite and xenotime are rare earth minerals (REMs) that are typical sources for rare earth elements (REEs). To advance the understanding of their leaching and precipitation behavior in different hydrometallurgical processes, Eh-pH diagrams were constructed and modified using the HSC 9.9 software. The aqueous stability of rare earth elements in H 2 O and acid leaching systems, i.e., the REE-Ligands-H 2 O systems, were depicted and studied based on the Eh-pH diagrams. This study considers the most relevant lixiviants, their resulting equilibrium states and the importance in the hydrometallurgical recovery of rare earth elements (REMs). A literature review was performed summarizing relevant Eh-pH diagrams and associated thermodynamic data. Shifting stability regions for REEs were discovered with additions of acid ligands and a narrow stability region for soluble REE-(SO 4 /Cl/NO 3 ) complexes under highly acidic conditions. As such, the recovery of REEs can be enhanced by adjusting pH and Eh values. In addition, the Eh-pH diagrams of the major contaminants (i.e., Fe, Ca and Al) in leaching systems were studied. The resulting Eh-pH diagrams provide possible insights into potential passivation on the particle surfaces due to the formation of an insoluble product layer.
A novel integrated-control architecture has been tested in nonlinear, one-dimensional simulations using the control-oriented transport simulator (COTSIM©) and in DIII-D experiments. Integrated architectures that can perform continuous-mission control while also handling off-normal events will be vital in future reactor-grade tokamaks. Continuous-mission controllers for individual magnetic and kinetic scalars (thermal stored-energy (W), volume-average toroidal rotation (Ω Φ ), and safety factor profile (q) at different spatial locations) have been integrated in this work with event-triggered neoclassical tearing-mode (NTM) suppression controllers by combining them into an architecture augmented by a supervisory and exception handling (S&EH) system and an actuator management (AM) system. Here, the AM system, which enables the integration of competing controllers, solves in real time a nonlinear optimization problem that takes into account the high-level control priorities dictated by the S&EH system. The resulting architecture offers a high level of integration and some of the functionalities that will be required to fulfill the advanced-control requirements anticipated for ITER. Initial simulations using COTSIM suggest that the plasma performance and its MHD stability may be improved under integrated feedback control. In addition, the integrated-control architecture has been implemented in the DIII-D plasma control system and tested experimentally for the first time ever in DIII-D in a high-q min scenario, which is a candidate for steady-state operation in ITER.
A major obstacle for machine learning (ML) in chemical science is the lack of physically informed feature representations that provide both accurate prediction and easy interpretability of the ML model. In this work, we describe adsorption systems using novel two-dimensional energy histogram (2D-EH) features, which are obtained from the probe-adsorbent energies and energy gradients at grid points located throughout the adsorbent. The 2D-EH features encode both energetic and structural information of the material and lead to highly accurate ML models (coefficient of determination R2 ~ 0.94–0.99) for predicting single-component adsorption capacity in metal–organic frameworks (MOFs). Here, we consider the adsorption of spherical molecules (Kr and Xe), linear alkanes with a wide range of aspect ratios (ethane, propane, n-butane, and n-hexane), and a branched alkane (2,2-dimethylbutane) over a wide range of temperatures and pressures. The interpretable 2D-EH features enable the ML model to learn the basic physics of adsorption in pores from the training data. We show that these MOF-data-trained ML models are transferrable to different families of amorphous nanoporous materials. We also identify several adsorption systems where capillary condensation occurs, and ML predictions are more challenging. Nevertheless, our 2D-EH features still outperform structural features including those derived from persistent homology. The novel 2D-EH features may help accelerate the discovery and design of advanced nanoporous materials using ML for gas storage and separation in the future.