Collision-induced and complex-mediated roaming mechanisms are revealed for an important bimolecular reaction in combustion.

Turbulent transport resulting from drift waves, typically, the ion temperature gradient (ITG) mode and trapped electron mode (TEM), is of great significance in magnetic confinement fusion. It is also well known that turbulence simulation is a challenging issue in both the complex physical model and huge CPU cost as well as long computation time. In this work, a credible turbulence transport prediction model, extended fluid code (ExFC-NN), based on a neural network (NN) approach is established using simulation data by performing an ExFC, in which multi-scale multi-mode fluctuations, such as ITG and TEM turbulence are involved. Results show that the characteristics of turbulent transport can be successfully predicted including the type of dominant turbulence and the radial averaged fluxes under any set of local gradient parameters. Furthermore, a global NN model can well reproduce the radial profiles of turbulence perturbation intensities and fluxes much faster than existing codes. A large number of comparative predictions show that the newly constructed NN model can realize rapid experimental analysis and provide reference data for experimental parameter design in the future.

Two reduced simulation approaches are exploited to predict the parametric boundary of dominant instability regime with global effects and the characteristics of corresponding turbulent particle fluxes in tokamak plasmas. One is usual numerical simulation of coexisting ion temperature gradient (ITG) mode and trapped electron mode (TEM) turbulence employing an extended fluid code (ExFC) based on the so-called Landau-Fluid model including the trapped electron dynamics. Here the density gradient (i.e. R/Ln) driven TEM (∇n-TEM) is emphasized. The other one is a surrogate turbulence transport model, taking a neural network (NN) based approach with speeding calculation. It is shown that the turbulent particle flux, particularly their directions depend on the type of micro-instability as ITG and/or TEM. On the other hand, the density gradient may govern the direction of the turbulent particle fluxes in general circumstances. Specifically, in the parameter regime explored here, the ITG and the electron temperature gradient driven TEM (∇Te-TEM) are destabilized for flat density profile, generally causing an inward particle flux, i.e., particle pinch. Contrarily, for steep density profile, the ∇n-TEM or coexisting ITG and TEM turbulence are dominant so that the particle always diffuses outwards. An empirical criterion is obtained to predict the dominant instability and the direction of particle flux for medium density gradients, involving the gradients of both ion and electron temperature as well as the density. These two transport models are applied to analyze the spontaneous excitation of a quasi-coherent mode (QCM) in the turbulence modulation discharge by MHD magnetic island observed on tokamak HL-2A, clearly showing a dynamic transition from ITG to TEM. Furthermore, the ExFC-NN model can predict and speed up the analysis of the turbulence transport in tokamak experiments.

The collisions transferring large portions of energy are often called supercollisions. In the H + C2H2 reactive system, the rovibrationally cold C2H2 molecule can be activated with substantial internal excitations by its collision with a translationally hot H atom. It is interesting to investigate the mechanisms of collisional energy transfer in other important reactions of H with hydrocarbons. Here, an accurate, global, full-dimensional potential energy surface (PES) of H + C2H4 was constructed by the fundamental invariant neural network fitting based on roughly 100 000 UCCSD(T)-F12a/aug-cc-pVTZ data points. Extensive quasi-classical trajectory calculations were carried out on the full-dimensional PES to investigate the energy transfer process in collisions of the translationally hot H atoms with C2H4 in a wide range of collision energies. The computed function of the energy-transfer probability is not a simple exponential decay function but exhibits large magnitudes in the region of a large amount of energy transfer, indicating the signature of supercollisions. The supercollisions among non-complex-forming nonreactive (prompt) trajectories are frustrated complex-forming processes in which the incoming H atom penetrates into C2H4 with a small C–H distance but promptly and directly leaves C2H4. The complex-forming supercollisions, in which either the attacking H atom leaves (complex-forming nonreactive collisions) or one of the original H atoms of C2H4 leaves (complex-forming reactive trajectories), dominate large energy transfer from the translational energy to internal excitation of molecule. The current work sheds valuable light on the energy transfer of this important reaction in the combustion and may motivate related experimental investigations.

Delta compression, which is efficient in removing repeated string among similar chunks, can be used as a complement to data deduplication in backup storage for extra space savings. The process of detecting similar candidates to use as the base for delta compression is called resemblance detection. Several indexes are required for resemblance detection. Maintaining them in RAM would limit the system scalability and increase system cost. Storing them on the disk suffers from low throughput due to poor random I/O performance of the disk. In this article, we present the history-aware resemblance detection (HARD), a cost-efficient resemblance detection approach that captures most of the similar chunks with a limited memory footprint. HARD is based on the observation that, for chunks in a backup, most of their similar chunks can be found in the most recent backups. HARD thus only indexes super-features in the most recent backups for resemblance detection to reduce the memory footprint of resemblance indexes while captures most of the potential similar chunks for delta compression.Experimental results based on three real-world datasets show that HARD achieves higher compression than the state-of-the-art approach.

A first dynamical study based on an accurate full-dimensional neural network PES for the CH2OO + H2O reaction.

The dissociative chemisorption of the N 2 molecule is the rate-limiting step in the ammonia synthesis process. Here, we carried out the full-dimensional quantum dynamics study for the dissociative chemisorption of N 2 on rigid Fe(111) based on a new, accurately fitted potential energy surface (PES). The computed dissociation probabilities reveal significant quantum effects for this heavy-diatomic reaction, as compared with the quasi-classical trajectory (QCT) results. This is due to the deep pretransition state adsorption well for this reaction, which also leads to the strong dynamical steering effects, as confirmed in the QCT calculations. The current magnitude of quantum and quasi-classical dissociation probabilities on a rigid surface agrees much better with the experimental data than the previous theoretical results with approximate surface atom motion treatment at incident energies lower than 4.0 eV. This is also the first time the full-dimensional quantum dynamics study is accomplished for the dissociative chemisorption of a heavy-diatomic molecule.

We report two novel roaming pathways for the H + C 2 H 2 → H 2 + C 2 H reaction by performing extensive quasiclassical trajectory calculations on a new, global, high-level machine learning-based potential energy surface. One corresponds to the acetylene-facilitated roaming pathway, where the H atom turns back from the acetylene + H channel and abstracts another H atom from acetylene. The other is the vinylidenefacilitated roaming, where the H atom turns back from the vinylidene + H channel and abstracts another H from vinylidene. The "double-roaming" pathways account for roughly 95% of the total cross section of the H 2 + C 2 H products at the collision energy of 70 kcal/ mol. These computational results give valuable insights into the significance of the two isomers (acetylene and vinylidene) in chemical reaction dynamics and also the experimental search for roaming dynamics in this bimolecular reaction.