Computational diagnostics for flame acceleration and transition to detonation in a hydrogen/air mixture

A new computational diagnostic method for pressure-induced compressibility is proposed by projecting its local contribution to the chemical explosive mode (CEM) in the chemical explosive mode analysis (CEMA) framework. The new method is validated for the study of detonation development during the deflagration-to-detonation transition (DDT) process. The flame characteristics are identified through the quantification of individual CEM contributions of chemical reaction, diffusion, and pressure-induced compressibility. Numerical simulations are performed to investigate the DDT processes in a stoichiometric hydrogen-air mixture. A Godunov algorithm, fifth-order in space, and third-order in time are used to solve the fully compressible Navier-Stokes equations on a dynamically adapting mesh. A single-step, calibrated chemical diffusive model (CDM) described by Arrhenius kinetics is used for energy release and conservation between the fuel and the product. The new diagnostic method is first applied to one-dimensional (1D) canonical flame configurations followed by two-dimensional (2D) simulations of DDT in an obstructed channel where different detonation initiation scenarios are examined using the new CEMA projection formulation. Detailed examinations of the idealized configuration of detonation initiation through shock focusing mechanism at a flame front are also studied using the new formulation. A comparison of the currently proposed CEMA projection and the original formulation by the authors suggests that including the pressure-induced compressibility is essential for the use of CEMA in DDT process. The results also show that the new formulation of CEMA projection can successively capture the detonation initiation through either a gradient mechanism or a direct initiation mechanism, and therefore can be used as an effective local analytical tool for the computational diagnostics of detonation initiation in a DDT process. It was found that detonation development is characterized by a strong contribution of chemistry role to the CEM which is pivotal to the initiation of detonation. The role of compressibility is found enhanced at the edge of the detonation front where diffusion was found to have minimal effects on detonation development

Effect of activation energy on detonation re-initiation behaviors in hydrogen-air mixtures

Two-dimensional simulations of a detonation propagating over a semi-cylinder in a channel filled with a stoichiometric hydrogen-air mixture are presented. A full set of Navier-Stokes equations is solved using a third-order WENO algorithm with HLLC flux, coupled with a calibrated, single-step chemical diffusive model (CDM). Simulation results using five different effective activation energies $\mathcal{E}=$ 4, 6, 10, 12 and 14 are presented featuring four distinct detonation attenuation regimes, including unattenuated detonation transmission ($\mathcal{E}=$ 4), critical detonation re-initiation ($\mathcal{E}=$ 6, and 10), cycled detonation re-initiation ($\mathcal{E}=$ 12), and complete quenching ($\mathcal{E}=$ 14). The degree of cell irregularity and the intensity of triple points are found positively correlated with the effective activation energy. With a low effective activation energy ($\mathcal{E}=$ 4), the CDM captures a regular cellular pattern, and the cellular structure remains intact as it propagates over the obstacle. With intermediate effective activation energies ($\mathcal{E}=$ 6, and 10), the detonation cell size increases and the cell structures become less regular with emerging multi-level cell structures. Here, a critical detonation re-initiation event is captured, where a strong transverse detonation wave forms following the Mach shock reflection, and eventually leads to a steady detonation propagation. At high effective activation energy ($\mathcal{E}=$ 12), the initial transverse detonations fail to produce a self-sustained detonation wave and multiple ignition and quenching events are found before the final establishment of the detonation wave

Giant Anomalous Hall and Nernst Effects in a Heavy Fermion Ferromagnet

The anomalous Hall and Nernst effects describe the voltage drop perpendicular to an applied current and temperature gradient due to the magnetization of a magnetic material. These effects can be utilized to measure the Berry curvature at the Fermi energy, and have potential applications in future electronic devices and thermoelectric energy conversion. In this paper, we report giant anomalous Hall conductivity and anomalous Nernst coefficient, as high as about 1000 $\Omega^{-1}$ cm$^{-1}$ and 10 $\mu$V K$^{-1}$, respectively, in a heavy fermion ferromagnet, CeCrGe$_3$. This compound uniquely manifests strong hybridization between the 4$f$ and conduction electrons, leading to a Kondo lattice state in the presence of ferromagnetic order. Unlike conventional topological semimetals in which the electron correlation is weak, CeCrGe$_3$ manifests a strong Berry curvature field of the heavy fermion with an extremely low Fermi energy. Our findings pave the way for exploring correlation-driven topological responses in a ferromagnetic Kondo lattice environment.Comment: 22 pages, 5 figure

A Quantitative Approach to Understand Self-Supervised Models as Cross-lingual Feature Extractors

In this work, we study the features extracted by English self-supervised learning (SSL) models in cross-lingual contexts and propose a new metric to predict the quality of feature representations. Using automatic speech recognition (ASR) as a downstream task, we analyze the effect of model size, training objectives, and model architecture on the models' performance as a feature extractor for a set of topologically diverse corpora. We develop a novel metric, the Phonetic-Syntax Ratio (PSR), to measure the phonetic and synthetic information in the extracted representations using deep generalized canonical correlation analysis. Results show the contrastive loss in the wav2vec2.0 objective facilitates more effective cross-lingual feature extraction. There is a positive correlation between PSR scores and ASR performance, suggesting that phonetic information extracted by monolingual SSL models can be used for downstream tasks in cross-lingual settings. The proposed metric is an effective indicator of the quality of the representations and can be useful for model selection.Comment: 12 pages, 5 figures, 4 table

Condensing Multilingual Knowledge with Lightweight Language-Specific Modules

Incorporating language-specific (LS) modules is a proven method to boost performance in multilingual machine translation. This approach bears similarity to Mixture-of-Experts (MoE) because it does not inflate FLOPs. However, the scalability of this approach to hundreds of languages (experts) tends to be unmanageable due to the prohibitive number of parameters introduced by full-rank matrices in fully-connected layers. In this work, we introduce the Language-Specific Matrix Synthesis (LMS) method. This approach constructs LS modules by generating low-rank matrices from two significantly smaller matrices to approximate the full-rank matrix. Furthermore, we condense multilingual knowledge from multiple LS modules into a single shared module with the Fuse Distillation (FD) technique to improve the efficiency of inference and model serialization. We show that our LMS method significantly outperforms previous LS methods and MoE methods with the same amount of extra parameters, e.g., 1.73 BLEU points over the Switch Transformer on many-to-many multilingual machine translation. Importantly, LMS is able to have comparable translation performance with much fewer parameters.Comment: Accepted at the main conference of EMNLP 202

Opportunistic Intermittent Control with Safety Guarantees for Autonomous Systems

Control schemes for autonomous systems are often designed in a way that anticipates the worst case in any situation. At runtime, however, there could exist opportunities to leverage the characteristics of specific environment and operation context for more efficient control. In this work, we develop an online intermittent-control framework that combines formal verification with model-based optimization and deep reinforcement learning to opportunistically skip certain control computation and actuation to save actuation energy and computational resources without compromising system safety. Experiments on an adaptive cruise control system demonstrate that our approach can achieve significant energy and computation savings

Dermatophagoides farinae microRNAs released to external environments via exosomes regulate inflammation-related gene expression in human bronchial epithelial cells

BackgroundDermatophagoides farinae (DFA) is an important species of house dust mites (HDMs) that causes allergic diseases. Previous studies have focused on allergens with protein components to explain the allergic effect of HDMs; however, there is little knowledge on the role of microRNAs (miRNAs) in the allergic effect of HDMs. This study aimed to unravel the new mechanism of dust mite sensitization from the perspective of cross-species transport of extracellular vesicles-encapsulated miRNAs from HDMs.MethodsSmall RNA (sRNA) sequencing was performed to detect miRNAs expression profiles from DFA, DFA-derived exosomes and DFA culture supernatants. A quantitative fluorescent real-time PCR (qPCR) assay was used to detect miRNAs expression in dust specimens. BEAS-2B cells endocytosed exosomes were modeled in vitro to detect miRNAs from DFA and the expression of related inflammatory factors. Representative dfa-miR-276-3p and dfa-novel-miR2 were transfected into BEAS-2B cells, and then differentially expressed genes (DEGs) were analyzed by RNA sequencing. Protein-protein interaction (PPI) network analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) terms enrichment analyses were performed on the first 300 nodes of DEGs.ResultssRNA sequencing identified 42 conserved miRNAs and 66 novel miRNAs in DFA, DFA-derived exosomes, and DFA culture supernatants. A homology analysis was performed on the top 18 conserved miRNAs with high expression levels. The presence of dust mites and miRNAs from HDMs in living environment were also validated. Following uptake of DFA-derived exosomes by BEAS-2B cells, exosomes transported miRNAs from DFA to target cells and produced pro-inflammatory effects in corresponding cells. RNA sequencing identified DEGs in dfa-miR-276-3p and dfa-novel-miR2 transfected BEAS-2B cells. GO and KEGG enrichment analyses revealed the role of exosomes with cross-species transporting of DFA miRNAs in inflammatory signaling pathways, such as JAK-STAT signaling pathway, PI3K/AKT signaling pathway and IL-6-mediated signaling pathway.ConclusionOur findings demonstrate the miRNAs expression profiles in DFA for the first time. The DFA miRNAs are delivered into living environments via exosomes, and engulfed by human bronchial epithelial cells, and cross-species regulation may contribute to inflammation-related processes