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Enabling high compression ratio in boosted spark ignition engines: Thermodynamic trajectory and fuel chemistry effects on knock
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A reconstructed discontinuous Galerkin method for compressible flows in Lagrangian formulation
In this work, a high-order accurate reconstructed discontinuous Galerkin (rDG) method is developed for solving two-dimensional hydrodynamic problems in cell-centered updated Lagrangian formulation. This method is the Lagrangian limit of the unsplit rDG-ALE formulation, and is obtained by assuming the equality of the grid velocity to the fluid velocity only at cell boundaries. The conservative variables and the Taylor basis defined on the time-dependent moving mesh, provide the piece-wise polynomial expansion in the updated Lagrangian formulation. A multi-directional nodal Riemann solver is implemented for computing the grid velocity at the vertices and the numerical flux at the cell boundaries. A characteristic limiting procedure is extended from the primitive variable version to the conservative variable version, and its performance is compared with the limiter on physical variables. A number of benchmark test cases are conducted to assess the accuracy, robustness, and non-oscillatory property of the DG(P0), DG(P1) and rDG(P1P2) methods. The numerical experiments demonstrate that the developed rDG method is able to attain the designed order of accuracy and the characteristic limiting procedure outperforms the limiter on physical variables in terms of the monotonicity and symmetry preservation for shock problems.
Relaxation of compressive stress in gamma-irradiated Viton™ fluoroelastomer
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Comprehensive quasi-steady force correlations for compressible flow through random particle suspensions
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A careful examination of closure models in Euler–Lagrange Simulations of compressible multiphase flow in a planar shock particle curtain problem
In this work we present a comprehensive investigation of state-of-the-art closure models employed to represent interphase momentum, thermal, and work exchange between the gas and particulate phases for Euler–Lagrange (EL) simulations in shock-driven flows. A complete list of closures for the force, torque, heat transfer, and work exchange models is provided. In particular, the present work includes a stochastic closure for the particle-to-particle variation in the quasi-steady force and a deterministic closure for particle-to-particle variation in the added mass force in an EL framework. These variations arise due to the presence of neighboring particles and particle–particle interactions. To investigate the importance of each closure term, we carry out fully three-dimensional simulations for a planar shock propagating over a random bed of inert particles. The primary goal is to evaluate the role of each closure term on the gas dynamic features (such as transmitted and reflected shock locations) and particle curtain features (such as upstream and downstream curtain locations). To this end, thirteen cases are considered, with each case progressively including a closure model with the goal to identify and quantify its contribution to the simulated dynamics. We show that the volume fraction dependence of the mean force models plays an important role in generating wave-like instabilities that lead to concentration bands. In addition, fluctuations in quasi-steady and added mass forces primarily decrease the internal instabilities that tend to enhance local volume fraction variations. Particle rotation is primarily due to inter-particle collisions, is generally weak, and does not play an important role in the translational dynamics for the present configuration. Inter-phase heat transfer has a strong effect on gas phase temperature, slows down the transmitted and reflected shocks, and decreases the width of the curtain. Furthermore, the absence of a work-coupling model fails to conserve the total energy, greatly under-predicts the gas temperature which in turn affects the particle dynamics.
Electrical energy storage using compressed gas in depleted hydraulically fractured wells
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Numerical assessment of fuel physical properties on high-dilution diesel advanced compression ignition combustion
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Optimal sensor placement for reconstructing wind pressure field around buildings using compressed sensing
Deciding how to optimally deploy sensors in a large, complex, and spatially extended structure is critical to ensure that the surface pressure field is accurately captured for subsequent analysis and design. In some cases, reconstruction of missing data is required in downstream tasks such as the development of digital twins. Here, this paper presents a data-driven sparse sensor selection algorithm, aiming to provide the most information contents for reconstructing aerodynamic characteristics of wind pressures over tall building structures parsimoniously. The algorithm first fits a set of basis functions to the training data, then applies a computationally efficient QR algorithm that ranks existing pressure sensors in order of importance based on the state reconstruction to this tailored basis. The findings of this study show that the proposed algorithm successfully re- constructs the aerodynamic characteristics of tall buildings from sparse measurement locations, generating stable and optimal solutions across a range of conditions. As a result, this study serves as a promising first step toward leveraging the success of data-driven and machine learning algorithms to supplement traditional genetic algorithms currently used in wind engineering.
Bridging the gap: Compressing non-unions for proper cellular signaling
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MGARD: A multigrid framework for high-performance, error-controlled data compression and refactoring
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Compression of a Stearic Acid Surfactant Layer on Water Investigated by Ambient Pressure X-ray Photoelectron Spectroscopy
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Anisotropic Hot Spot Formation at a Grain Boundary in Shock-Compressed TATB High Explosive Crystal
Secondary high explosives (HEs) exhibit rich microstructure that promotes the formation of hot spots responsible for detonation initiation, but the role of microstructural interfaces remains poorly quantified. To this end, we develop extensions for the generalized crystal-cutting method (GCCM) to prepare molecular dynamics (MD) simulation cells containing grain boundaries (GBs) and other crystal–crystal interfaces with prescribed tilt and twist orientations. Using the GCCM, we perform MD simulations of shock interactions with a GB between the (001) and (100) crystal facets in the secondary HE TATB (1,3,5-triamino-2,4,6-trinitrobenzene). Our MD simulations reveal a strong directional dependence to the formation of a hot spot at the GB interface. In particular, transmission of the shock from the (001) grain to the (100) grain yields a hot spot in the (100) grain at the GB interface, whereas no hot spot is produced when an equivalent shock transits the GB in the opposite direction. We trace the origin of this GB anisotropy to three dominant factors: (1) the intrinsic differences in shock-deformation mechanisms and wave structures for the bulk (100) and (001) grains, which leads to distinct geometries and mechanical impedances upon shock arrival to the GB depending on which grains are donor or acceptor for the transmitted shock; (2) the different time intervals separating the initial shock rise and the formation of steady wave structures in the respective donor–acceptor configurations; and (3) the differences in time scales required to re-establish local thermal equilibrium. Interfacial hot spots form when these factors combine to impede development of a steady two-wave structure and instead induce a localized, pseudosingly shocked region that undergoes a higher rate of work production (resulting in a higher temperature) compared to when the steady two-wave structure develops further from the interface. The extensions to the GCCM approach presented here are anticipated to facilitate a wide range of MD studies that focus on understanding the role of crystal–crystal interfaces in molecular materials.
Algal Biochar-Metal Nanocomposite Particles Tailor the Hydration Kinetics and Compressive Strength of Portland Cement Paste
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Quasi-One-Dimensional Metallicity in Compressed CsSnI 3
Low-dimensional metal halides exhibit strong structural and electronic anisotropies, making them candidates for accessing unusual electronic properties. Here, in this work, we demonstrate pressure-induced quasi-one-dimensional (quasi-1D) metallicity in δ-CsSnI 3 . With the application of pressure up to 40 GPa, the initially insulating δ-CsSnI 3 transforms to a metallic state. Synchrotron X-ray diffraction and Raman spectroscopy indicate that the starting 1D chain structure of edge-sharing Sn–I octahedra in δ-CsSnI 3 is maintained in the high-pressure metallic phase while the SnI 6 octahedral chains are distorted. Our experiments combined with first-principles density functional theory calculations reveal that pressure induces Sn–Sn hybridization and enhances Sn–I coupling within the chain, leading to band gap closure and formation of conductive SnI 6 distorted octahedral chains. In contrast, the interchain I...I interactions remain minimal, resulting in a highly anisotropic electronic structure and quasi-1D metallicity. Our study offers a high-pressure approach for achieving diverse electronic platforms in the broad family of low-dimensional metal halides.
A multi-wavelength streaked optical pyrometer for dynamic shock compression measurements above 2500 K
This work presents a concept and implementation of a calibrated streaked spectral pyrometer (SSP) temperature diagnostic used in dynamically driven shock experiments on a two-stage gas gun. This system relies upon measuring the total system response using a tunable monochromator, a NIST-traceable calibrated power meter, and a SSP. The diagnostic performance is validated against previously measured temperatures of shock driven z-cut quartz at 99 GPa and 93 GPa. The results are found to agree with the literature to within 5% and are discussed in this manuscript. The experimental setup utilizes measurements from multiple SSP systems per sample, providing several independent measurements and substantially increasing confidence in the extrapolated shock driven sample temperature.
Dynamic compression of TiO 2 to 221 GPa
The high-pressure dynamic response of titanium dioxide (TiO 2 ) is not only of interest because of its numerous industrial applications but also because of its structural similarities to silica (SiO 2 ). We performed plate impact experiments in a two-stage light gas gun, at peak stresses from 64 to 221 GPa to determine the TiO 2 response along the Hugoniot. The lower stress experiment at 64 GPa shows an elastic behavior followed by an elastic–plastic transition, whereas the high stress experiments above 64 GPa show a single wave structure. Previous shock studies have shown the presence of high-pressure phases (HPP) I (26 GPa) and HPP II (100 GPa); however, our data suggest that the HPP I phase is stable up to 150 GPa. Using a combination of data from our current study and our previous Z-data, we determine that TiO 2 likely melts on the Hugoniot at 157 GPa. Furthermore, our data confirm that TiO 2 is not highly incompressible as shown by a previous study.
Atomic cluster expansion potential for large scale simulations of hydrocarbons under shock compression
We present an Atomic Cluster Expansion (ACE) machine learned potential developed for high-fidelity atomistic simulations of hydrocarbons, targeting pressures and temperatures near and above supercritical fluid regimes for molecular fluids. A diverse set of stoichiometries were covered in training, including 1:0 (pure carbon), 1:4 (methane), and 1:1 (benzene), and rich bonding environments sampled at supercritical temperatures, hydrogen rich, reactive mixtures where metastable stoichiometries arise, including 1:2 (ethylene) and 1:3 (ethane). A high-fidelity training database was constructed by performing large-scale quantum molecular dynamic simulations [density functional theory (DFT) MD] of diamond, graphite, methane, and benzene. A novel approach to selecting structures from DFT MD is also presented, which allows for the rapid selection of unique DFT MD frames from complex trajectories. Comparisons to DFT and experimental data demonstrate that the presented ACE potential accurately reproduces isotherms, carbon melting curves, radial distribution functions, and shock Hugoniots for carbon and hydrocarbon systems for pressures up to 100 GPa and temperatures up to 6000 K for hydrocarbon systems and up to 9000 K for pure carbon systems. This work delivers a potential that can be used for accurate, large-scale simulations of shocked hydrocarbons and demonstrates a methodology for fitting and validating machine learning interatomic potentials to complex molecular environments, which can be applied to energetic materials in future works.