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Performance characteristics of gaseous secondary injection thrust vector control in overexpanded primary nozzles under sea level conditions
The behavior of energetic ions is fundamentally important in the study of fusion plasmas. While well-studied in tokamak, spherical torus, and stellarator plasmas, relatively little is known in reversed field pinch plasmas about the dynamics of fast ions and the effects they cause as a large population. These studies are now underway in the Madison Symmetric Torus with an intense 25 keV, 1 MW hydrogen neutral beam injector (NBI). Measurements of the time-resolved fast ion distribution via a high energy neutral particle analyzer, as well as beam-target neutron flux (when NBI fuel is doped with 3–5% D{sub 2}) both demonstrate that at low concentration the fast ion population is consistent with classical slowing of the fast ions, negligible cross-field transport, and charge exchange as the dominant ion loss mechanism. A significant population of fast ions develops; simulations predict a super-Alfvénic ion density of up to 25% of the electron density with both a significant velocity space gradient and a sharp radial density gradient. There are several effects on the background plasma including enhanced toroidal rotation, electron heating, and an altered current density profile. The abundant fast particles affect the plasma stability. Fast ions at the island of the core-most resonant tearing mode have a stabilizing effect, and up to 60% reduction in the magnetic fluctuation amplitude is observed during NBI. The sharp reduction in amplitude, however, has little effect on the underlying magnetic island structure. Simultaneously, beam driven instabilities are observed as repetitive ∼50 μs bursts which coincide with fast particle redistribution; data indicate a saturated core fast ion density well below purely classical predictions.
Tests are ongoing to conduct {approx}20 MA z-pinch implosions on the Z accelerator at Sandia National Laboratory using Ar, Kr, and D{sub 2} gas puffs as the imploding loads. The relatively high cost of operations on a machine of this scale imposes stringent requirements on the functionality, reliability, and safety of gas puff hardware. Here we describe the development of a prototype gas puff system including the multiple-shell nozzles, electromagnetic drivers for each nozzle's valve, a UV pre-ionizer, and an inductive isolator to isolate the {approx}2.4 MV machine voltage pulse present at the gas load from the necessary electrical and fluid connections made to the puff system from outside the Z vacuum chamber. This paper shows how the assembly couples to the overall Z system and presents data taken to validate the functionality of the overall system.
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To further improve the energy conversion efficiency of internal combustion engine, the transient and complex air flow movement inside the cylinder needs to be better understood and controlled. Although the in-cylinder flow fields are highly stochastic with strong cycle-to-cycle fluctuations, machine learning can still provide an efficient way to learn and regress the complex flow movement process inside the cylinder. In this work, a bidirectional recurrent neural network (bi-RNN) model with long short-term memory was applied to predict the in-cylinder flow fields at different time steps using training data from mull-cycle particle image velocimetry (PIV) measurements. To evaluate the agreement between the true and predicted flow fields, structure and magnitude comparison indices are calculated both globally and locally. The comparison results show that the bi-RNN model can accurately predict the bulk flow and vortex motions from early intake stroke to compression stroke. This work demonstrates that the machine learning model has the potential to predict the underlying dynamics of the interaction between in-cylinder flows and provides a reliable way to improve temporal resolution in PIV flow data to better reveal transient in-cylinder flow features.
Converting biomass to a useful fuel commonly incorporates the pyrolysis of the biomass feed stock. The base liquid fraction usually contains high concentrations of ketones, aldehydes and carboxylic acids, of which each can cause detrimental issues related to the storage and upgrading process. Knowing the carbonyl species and the concentration of each will provide value information to the pyrolysis researchers, specifically as that community branches into more targeted end-products such as jet fuel or biogenic-derived oxygenate-containing fuel products. The analysis of aldehydes, ketones and small alkyl carboxylic acids using 2,4-dinitrophenylhydrazine (DNPH) derivation method has been well documented and the method is commonly used the analytical community. By using liquid chromatograph coupled to tandem mass spectrometry, biomass sample analysis can be complete with identification of most carbonyl species. The issue of identifying isobaric ketone and aldehyde compounds can be resolved by utilizing differences in retention time or characteristic fragment ions of ketones and aldehydes. One issue which could not resolved using published methods was identifying aromatic or large non-aromatic carboxylic acids from their corresponding hydroxyl aldehyde or ketone analogs. By modifying the current method for determining carbonyls in biomass samples, carboxylic and hydroxyl-carbonyl can be determined. A careful adjustment of the pH during the extraction procedure and extended heating time of the DNPH solution allowed for the successful derivation of aromatic carboxylic acids. Like other dinitrophenylhydrazones, carboxylic acid derivatives also produce a unique secondary ion pattern, which was useful to distinguish these species from the non-acid analogs.
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