Physics-informed neural networks of the Saint-Venant equations for downscaling a large-scale river model

Large-scale river models are being refined over coastal regions to improve the scientific understanding of coastal processes, hazards and responses to climate change. However, coarse mesh resolutions and approximations in physical representations of tidal rivers limit the performance of such models at resolving the complex flow dynamics especially near the river-ocean interface, resulting in inaccurate simulations of flood inundation. In this research, we propose a machine learning (ML) framework based on the state-of-the-art physics-informed neural network (PINN) to simulate the downscaled flow at the subgrid scale. First, we demonstrate that PINN is able to assimilate observations of various types and solve the one-dimensional (1-D) Saint-Venant equations (SVE) directly. We perform the flow simulations over a floodplain and along an open channel in several synthetic case studies. The PINN performance is evaluated against analytical solutions and numerical models. Our results indicate that the PINN solutions of water depth have satisfactory accuracy with limited observations assimilated. In the case of flood wave propagation induced by storm surge and tide, a new neural network architecture is proposed based on Fourier feature embeddings that seamlessly encodes the periodic tidal boundary condition in the PINN's formulation. Furthermore, we show that the PINN-based downscaling can produce more reasonable subgrid solutions of the along-channel water depth by assimilating observational data. The PINN solution outperforms the simple linear interpolation in resolving the topography and dynamic flow regimes at the subgrid scale. This study provides a promising path towards improving emulation capabilities in large-scale models to characterize fine-scale coastal processes

Nanocutting mechanism of 6H-SiC investigated by scanning electron microscope online observation and stress-assisted and ion implant-assisted approaches

Nanocutting mechanism of single crystal 6H-SiC is investigated through a novel scanning electron microscope setup in this paper. Various undeformed chip thicknesses on (0001) orientation are adopted in the nanocutting experiments. Phase transformation and dislocation activities involved in the 6H-SiC nanocutting process are also characterized and analyzed. Two methods of stress-assisted and ion implant-assisted nanocutting are studied to improve 6H-SiC ductile machining ability. Results show that stress-assisted method can effectively decrease the hydrostatic stress and help to activate dislocation motion and ductile machining; ion implant-induced damages are helpful to improve the ductile machining ability from MD simulation and continuous nanocutting experiments under the online observation platform.Peer reviewe

Molecular characterization of ultrafine particles using extractive electrospray time-of-flight mass spectrometry

Publisher Copyright: © 2021 The Author(s).Aerosol particles negatively affect human health while also having climatic relevance due to, for example, their ability to act as cloud condensation nuclei. Ultrafine particles (diameter Dp < 100 nm) typically comprise the largest fraction of the total number concentration, however, their chemical characterization is difficult because of their low mass. Using an extractive electrospray time-of-flight mass spectrometer (EESI-TOF), we characterize the molecular composition of freshly nucleated particles from naphthalene and b-caryophyllene oxidation products at the CLOUD chamber at CERN. We perform a detailed intercomparison of the organic aerosol chemical composition measured by the EESI-TOF and an iodide adduct chemical ionization mass spectrometer equipped with a filter inlet for gases and aerosols (FIGAERO-I-CIMS). We also use an aerosol growth model based on the condensation of organic vapors to show that the chemical composition measured by the EESI-TOF is consistent with the expected condensed oxidation products. This agreement could be further improved by constraining the EESI-TOF compound-specific sensitivity or considering condensed-phase processes. Our results show that the EESI-TOF can obtain the chemical composition of particles as small as 20 nm in diameter with mass loadings as low as hundreds of ng m_3 in real time. This was until now difficult to achieve, as other online instruments are often limited by size cutoffs, ionization/thermal fragmentation and/or semicontinuous sampling. Using real-time simultaneous gas- and particle-phase data, we discuss the condensation of naphthalene oxidation products on a molecular level.Peer reviewe

Survival of newly formed particles in haze conditions

Intense new particle formation events are regularly observed under highly polluted conditions, despite the high loss rates of nucleated clusters. Higher than expected cluster survival probability implies either ineffective scavenging by pre-existing particles or missing growth mechanisms. Here we present experiments performed in the CLOUD chamber at CERN showing particle formation from a mixture of anthropogenic vapours, under condensation sinks typical of haze conditions, up to 0.1 s(-1). We find that new particle formation rates substantially decrease at higher concentrations of pre-existing particles, demonstrating experimentally for the first time that molecular clusters are efficiently scavenged by larger sized particles. Additionally, we demonstrate that in the presence of supersaturated gas-phase nitric acid (HNO3) and ammonia (NH3), freshly nucleated particles can grow extremely rapidly, maintaining a high particle number concentration, even in the presence of a high condensation sink. Such high growth rates may explain the high survival probability of freshly formed particles under haze conditions. We identify under what typical urban conditions HNO3 and NH3 can be expected to contribute to particle survival during haze.Peer reviewe

An intercomparison study of four different techniques for measuring the chemical composition of nanoparticles

Currently, the complete chemical characterization of nanoparticles (< 100 nm) represents an analytical challenge, since these particles are abundant in number but have negligible mass. Several methods for particle-phase characterization have been recently developed to better detect and infer more accurately the sources and fates of sub-100 nm particles, but a detailed comparison of different approaches is missing. Here we report on the chemical composition of secondary organic aerosol (SOA) nanoparticles from experimental studies of α-pinene ozonolysis at −50, −30, and −10 ∘C and intercompare the results measured by different techniques. The experiments were performed at the Cosmics Leaving OUtdoor Droplets (CLOUD) chamber at the European Organization for Nuclear Research (CERN). The chemical composition was measured simultaneously by four different techniques: (1) thermal desorption–differential mobility analyzer (TD–DMA) coupled to a NO$^-_3$ chemical ionization–atmospheric-pressure-interface–time-of-flight (CI–APi–TOF) mass spectrometer, (2) filter inlet for gases and aerosols (FIGAERO) coupled to an I$^−$ high-resolution time-of-flight chemical ionization mass spectrometer (HRToF-CIMS), (3) extractive electrospray Na$^+$ ionization time-of-flight mass spectrometer (EESI-TOF), and (4) offline analysis of filters (FILTER) using ultra-high-performance liquid chromatography (UHPLC) and heated electrospray ionization (HESI) coupled to an Orbitrap high-resolution mass spectrometer (HRMS). Intercomparison was performed by contrasting the observed chemical composition as a function of oxidation state and carbon number, by estimating the volatility and comparing the fraction of volatility classes, and by comparing the thermal desorption behavior (for the thermal desorption techniques: TD–DMA and FIGAERO) and performing positive matrix factorization (PMF) analysis for the thermograms. We found that the methods generally agree on the most important compounds that are found in the nanoparticles. However, they do see different parts of the organic spectrum. We suggest potential explanations for these differences: thermal decomposition, aging, sampling artifacts, etc. We applied PMF analysis and found insights of thermal decomposition in the TD–DMA and the FIGAERO

Survival of newly formed particles in haze conditions

Intense new particle formation events are regularly observed under highly polluted conditions, despite the high loss rates of nucleated clusters. Higher than expected cluster survival probability implies either ineffective scavenging by pre-existing particles or missing growth mechanisms. Here we present experiments performed in the CLOUD chamber at CERN showing particle formation from a mixture of anthropogenic vapours, under condensation sinks typical of haze conditions, up to 0.1 s(-1). We find that new particle formation rates substantially decrease at higher concentrations of pre-existing particles, demonstrating experimentally for the first time that molecular clusters are efficiently scavenged by larger sized particles. Additionally, we demonstrate that in the presence of supersaturated gas-phase nitric acid (HNO3) and ammonia (NH3), freshly nucleated particles can grow extremely rapidly, maintaining a high particle number concentration, even in the presence of a high condensation sink. Such high growth rates may explain the high survival probability of freshly formed particles under haze conditions. We identify under what typical urban conditions HNO3 and NH3 can be expected to contribute to particle survival during haze.Peer reviewe