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Shock-induced energy localization is a crucial mechanism for determining shock sensitivity of energetic materials (EMs). Hotspots, i.e., localized areas of elevated temperature, arise when shocks interact with defects (cracks, pores, and interfaces) in the EM microstructure. The ignition and growth of hotspots in a shocked energetic material contribute to rapid chemical reactions that can couple with the passing shock wave, potentially leading to a self-sustained detonation wave. Predictive models for shock-to-detonation transition must correctly capture hotspot dynamics, which demands high-fidelity material models for meso-scale calculations. In this work, we deploy atomistics-guided material models for the energetic crystal RDX (1,3,5-trinitro-1,3,5-triazinane) and perform tandem continuum and all-atom molecular dynamics (MD) simulations. The computational setup for the continuum and MD simulations are nearly identical. The material models used for the calculations are derived from MD data, particularly the equations of state, rate-dependent Johnson–Cook strength model, and pressure-dependent shear modulus and melting temperature. We show that a modified Johnson–Cook model that accounts for shear-induced localization at the pore surface is necessary to represent well—relative to MD as the ground truth—the inelastic response of the crystal under a range of shock conditions. A head-to-head comparison of continuum and atomistic calculations across several metrics of pore collapse and energy deposition demonstrates that the continuum calculations are in good overall agreement with MD. Therefore, this work provides improved RDX material models to perform physically accurate meso-scale simulations, to enhance understanding of hot spot formation, and to use meso-scale hot spot data to inform macro-scale shock simulations. [ABSTRACT FROM AUTHOR]

Meso-scale calculations of energy localization and initiation in energetic material microstructures must capture the deformation and collapse of pores and high-temperature shear bands, which lead to hotspots. Because chemical reaction rates depend sensitively on temperature, predictive continuum models need to get the pore-collapse dynamics and resulting hotspot temperatures right; this imposes stringent demands on the fidelity of thermophysical model forms and parameters and on the numerical methods employed to perform high-resolution meso-scale calculations. Here, continuum material models for β-HMX are examined in the context of shock-induced pore collapse, treating predictions from all-atom molecular dynamics (MD) simulations as ground truth. Using atomistics-consistent material properties, we show that the currently available strength models for HMX fail to correctly capture pore collapse and hotspot temperatures. Insights from MD are then employed to advance a Modified Johnson–Cook (M-JC) strength model, which is shown to capture key aspects of the physics of shock-induced localization in HMX. The study culminates in a MD-guided strength model for β-HMX that produces continuum pore-collapse results in better alignment on several aspects with those predicted by MD, including pore-collapse mechanism and rate, shear-band formation in the collapse zone, and temperature, strain, and stress fields in the hotspot zone and the surrounding material. The resulting MD-informed/MD-determined M-JC model should improve the fidelity of meso-scale simulations to predict the detonation initiation of HMX-based energetic materials in microstructure-aware multi-scale frameworks. [ABSTRACT FROM AUTHOR]

High-fidelity constitutive models are critical for accurate meso-scale continuum modeling and prediction of shock initiation of crystalline energetic materials (EMs). While empirically calibrated or atomistic-guided anisotropic elastoplastic models of EM such as cyclotetramethylene-tetranitramine (HMX) and cyclotrimethylene-trinitramine (RDX) capture important micromechanical phenomena (such as dislocation evolution, slip-resistance, and anisotropic elasticity), the computational cost of using anisotropic single-crystal plasticity models can become prohibitive for meso-scale computations of void-collapse and hotspot formation in microstructures. Thermo-mechanically representative, isotropic, pressure, temperature, and rate-dependent material constitutive models are practical alternatives for meso-scale simulations of the shock response of microstructures. To this end, this work constructs physically consistent isotropic plasticity from anisotropic single-crystal plasticity models for HMX and RDX. State-of-the-art crystal plasticity models for HMX and RDX are used to compute the stress states in single crystals oriented in three different directions relative to shocks generated by impact at velocities ranging from 100 to 1000 m/s. Post-shock von Mises stress fields for the three orientations are then used to calibrate the strain-rate hardening coefficient and the reference strain rate for a rate-dependent Johnson–Cook (JC) yield surface model. We compare the pressures and the post-shock von Mises stresses between the JC and the anisotropic models to show that the isotropic computations closely approximate the averaged deformation response of the three different crystal orientations. We then model the interaction of a shock generated by a 500 m/s impact with a 0.5 μm void and show that the pressures and the deviatoric stresses obtained using the isotropic model closely match those computed from anisotropic models for both HMX and RDX. The resulting isotropic J2 plastic flow model for HMX and RDX can be employed to perform meso-scale simulations for energy localization due to shear bands and void collapse in the two materials. [ABSTRACT FROM AUTHOR]

Multi-scale predictive models for the shock sensitivity of energetic materials connect energy localization ("hotspots") in the microstructure to macro-scale detonation phenomena. Calculations of hotspot ignition and growth rely on models for chemical reaction rates expressed in Arrhenius forms; these chemical kinetic models, therefore, are foundational to the construction of physics-based, simulation-derived meso-informed closure (reactive burn) models. However, even for commonly used energetic materials (e.g., HMX in this paper) there are a wide variety of reaction rate models available. These available reaction rate models produce reaction time scales that vary by several orders of magnitude. From a multi-scale modeling standpoint, it is important to determine which model best represents the reactive response of the material. In this paper, we examine three global Arrhenius-form rate models that span the range of reaction time scales, namely, the Tarver 3-equation, the Henson 1-equation, and the Menikoff 1-equation models. They are employed in a meso-informed ignition and growth model which allows for connecting meso-scale hotspot dynamics to macro-scale shock-to-detonation transition. The ability of the three reaction models to reproduce experimentally observed sensitivity is assessed by comparing the predicted criticality envelope (Walker–Wasley curve) with experimental data for pressed HMX Class V microstructures. The results provide a guideline for model developers on the plausible range of time-to-ignition that are produced by physically correct Arrhenius rate models for HMX. [ABSTRACT FROM AUTHOR]

Reactive burn models for heterogeneous energetic materials (EMs) must account for chemistry as well as microstructure to predict shock-to-detonation transition (SDT). Upon shock loading, the collapse of individual voids leads to ignition of hotspots, which then grow and interact to consume the surrounding material. The sub-grid dynamics of shock-void interactions and hotspot development are transmitted to macro-scale SDT calculations in the form of a global reactive "burn model." This paper presents a physically evocative model, called meso-informed sub-grid source terms for energy localization (MISSEL), to close the macro-scale governing equations for calculating SDT. The model parameters are explicitly related to four measurable physical quantities: two depending on the microstructure (the porosity ϕ and average pore size D ¯ v o i d ), one depending on shock–microstructure interaction (the fraction of critical voids ξ c r ), and the other depending on the chemistry (the burn front velocity V h s ). These quantities are individually quantifiable using a small number of rather inexpensive meso-scale simulations. As constructed, the model overcomes the following problems that hinder the development of meso-informed burn models: (1) the opacity of more sophisticated surrogate/machine-learning approaches for bridging meso- and macro-scales, (2) the rather large number of high-resolution mesoscale simulations necessary to train machine-learning algorithms, and (3) the need for calibration of many free parameters that appear in phenomenological burn models. The model is tested against experimental data on James curves for a specific class of pressed 1,3,5,7-tetranitro-1,3,5,7-tetrazoctane materials. The simple, evocative, and fast-to-construct MISSEL model suggests a route to develop frameworks for physics-informed, simulation-derived meso-informed burn models. [ABSTRACT FROM AUTHOR]

Sensitivity in polymer-bonded explosives (PBXs) relies on the presence of defects, such as cracks and voids, which create localized thermal energy, commonly known as hotspots, and initiate reactions through various localization phenomena. Our prior research has explored the use of internal gas pressure induced by thermite ignition to generate localized defects for PBX sensitization. However, further research is required to gain a more comprehensive understanding of the defect generation process resulting from internal gas pressure. This study investigates the process of defect generation in PBXs in response to internally induced gas pressure by applying controlled compressed gas to a fabricated cavity within the materials, simulating the gas pressure emitting from thermite. X-ray micro-computed tomography was employed to visualize the microstructure of the sample before and after gas injection. The experiments reveal the significance of gas pressure, cavity shape, temperature, and specimen compaction pressure in the defect generation. Numerical simulations using Abaqus/Standard were conducted to assess the defect generation in mock PBXs under varying gas pressures, cohesive properties, and binder thicknesses. The simulation results demonstrate the substantial influence of these properties on the ability to generate defects in mock PBXs. This study contributes to a better understanding of the factors influencing defect generation in mock PBXs. This knowledge is crucial for achieving precise control over defect generation, leading to improved ignition and detonation characteristics in PBXs. [ABSTRACT FROM AUTHOR]

This paper demonstrates a novel Eulerian computational framework for modeling anisotropic elastoplastic deformations of organic crystalline energetic materials (EM) under shocks. While Eulerian formulations are advantageous for handling large deformations, constitutive laws in such formulations have been limited to isotropic elastoplastic models, which may not fully capture the shock response of crystalline EM. The present Eulerian framework for high-strain rates, large deformation material dynamics of EM incorporates anisotropic isochoric elasticity via a hypo-elastic constitutive law and visco-plastic single-crystal models. The calculations are validated against atomistic calculations and experimental data and benchmarked against Lagrangian (finite element) crystal plasticity computations for shock-propagation in a monoclinic organic crystal, octahydro-1,3,5,7-tetranitro-1,3,5,7 tetrazocine (β-HMX). The Cauchy stress components and the resolved shear stresses calculated using the present Eulerian approach are shown to be in good agreement with the Lagrangian computations for different crystal orientations. The Eulerian framework is then used for computations of shock-induced inert void collapse in β-HMX to study the effects of crystal orientations on hotspot formation under different loading intensities. The computations show that the hotspot temperature distributions and the collapse profiles are sensitive to the crystal orientations at lower impact velocities (viz., 500 m/s); when the impact velocity is increased to 1000 m/s, the collapse is predominantly hydrodynamic and the role of anisotropy is modest. The present methodology will be useful to simulate energy localization in shocked porous energetic material microstructures and other situations where large deformations of single and polycrystals govern the thermomechanical response. [ABSTRACT FROM AUTHOR]

Heterogeneous energetic materials (EMs) contain microstructural defects such as voids, cracks, interfaces, and delaminated zones. Under shock loading, these defects offer potential sites for energy localization, i.e., hotspot formation. In a porous EM, the collapse of one void can generate propagating blast waves and hotspots that can influence the hotspot phenomena at neighboring voids. Such void–void interactions must be accounted for in predictive multi-scale models for the reactive response of a porous EM. To infuse such meso-scale phenomena into a multi-scale framework, a meso-informed ignition and growth model (MES-IG) has been developed, where the influence of void–void interactions is incorporated into the overall reaction rate through a function, f v − v . Previously, MES-IG was applied to predict the sensitivity and reactive response of EM, where f v − v was assumed to be a function of the overall sample porosity alone. This paper performs a deeper analysis to model the strong dependency of f v − v on other factors, such as void size and shock strength. The improved model for void–void interactions produces good agreement with direct numerical simulations of the HE microstructures and, thus, advances the predictive capability of multi-scale models of the shock response and sensitivity of EM. [ABSTRACT FROM AUTHOR]

Head-to-head comparisons of multiple experimental observations and numerical simulations on a deconstructed plastic-bonded explosive consisting of an octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine crystal embedded in a polymeric binder with a 4 ns duration 20 GPa input shock are presented. Hot spots observed in high-resolution direct numerical simulations are compared with micro-scale shock-induced reactions visualized using nanosecond microscope imaging and optical pyrometry. Despite the challenges and limitations of both the experimental and simulation techniques, an agreement is obtained on many of the observed features of hot spot evolution, e.g., (1) the magnitude and time variation of temperatures in the hot spots, (2) the time to fully consume the crystals (∼100 ns) of size (100–300 μm) employed in this study, and (3) the locations of hot spot initiation and growth. Three different mechanisms of hot spot formation are indicated by simulations: (1) high-temperature hot spots formed by pore collapse, (2) lower temperature hot spots initiated at the polymer–crystal interface near corners and asperities, and (3) high-temperature reaction waves leading to fast consumption of the energetic crystal. This first attempt at a head-to-head comparison between experiments and simulations not only provides new insight but also highlights efforts needed to bring models and experiments into closer alignment, in particular, highlighting the importance of distinctly three-dimensional and multiple mechanisms of the hot spot ignition and growth. [ABSTRACT FROM AUTHOR]

The response of a wide class of heterogeneous energetic materials (HEs) to loads is determined by dynamics at the meso-scale, i.e., by physicochemical processes in their underlying microstructure. Structure–property–performance (S–P–P) linkages for such materials can be developed in a multi-scale framework, connecting the physics and thermophysical properties at the meso-scale to response at the macro-scale. Due to the inherent stochasticity of the microstructure, ensembles of microstructures are required to conduct meso-scale simulations to establish S–P–P linkages. Here, a deep neural network-based method called deep feature representation is applied to generate a range of material microstructures from heterogeneous energetic materials to metal foams and metallic mixtures. The method allows for the generation of stochastic microstructures using a single real microstructure as the input and is not limited to low packing density or topological complexity of solids. In its application to pressed energetic materials, we show that qualitative and quantitative features of real (i.e., imaged) microstructures are captured in the synthetic microstructures. Therefore, a stochastic ensemble of synthetic microstructures can be created for use in reactive meso-scale simulations to relate the microstructures of HEs to their performance. While the focus is on pressed HE microstructures, we also show that the method is general and useful for generating microstructures for in silico experiments for a wide range of composite/multiphase materials, which can be used to establish S–P–P linkages. [ABSTRACT FROM AUTHOR]

Meso-scale simulations of pressed energetic materials are performed using synthetic microstructures generated using deep feature representation, a deep convolutional neural network-based approach. Synthetic microstructures are shown to mimic real microstructures in the statistical representation of global and local features of micro-morphology for three different classes of pressed HMX with distinctive micro-structural characteristics. Direct numerical simulations of shock-loaded synthetic microstructures are performed to calculate the meso-scale reaction rates. For all three classes, the synthetic microstructures capture the effect of morphological uncertainties of real microstructures on the response to shock loading. The calculated reaction rates for different classes also compare well with those of the corresponding real microstructures. Thus, the article demonstrates that machine-generated ensembles of synthetic microstructures can be employed to derive structure–property–performance linkages of a wide class of real pressed energetic materials. The ability to manipulate the synthetic microstructures using deep learning-based approaches then provides an opportunity for material designers to develop and manufacture pressed energetic materials that can yield targeted performance. [ABSTRACT FROM AUTHOR]

Accurate simulations of the shock response of heterogeneous energetic (HE) materials require closure models, which account for energy localization in the micro-structure. In a multi-scale framework, closure is provided by reaction rate models that account for ignition and growth of hotspots, allowing for prediction of the overall macro-scale sensitivity of a HE material. In the present meso-informed ignition and growth (MES-IG) model, the reaction rate is expressed as a function of shock pressure and morphology of the void field in a pressed energetic material. In MES-IG, the void morphology is quantified in terms of a limited number of parameters: viz., overall porosity, void size, and shape (aspect ratio and orientation). In this paper, we quantify the effects of arbitrary variations in void shapes on meso-scale energy deposition rates. A collection of voids of arbitrary shapes is extracted from scanning electron microscope (SEM) images of real, pressed HMX (octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine) samples and classified into groups based on their similarity in shapes. Direct numerical simulations (DNS) are performed on the highly contorted "real" void shapes, and the calculated hotspot ignition and growth rates are compared with values predicted by the MES-IG. It is found that while the parameterization of complex void morphologies in terms of orientation and aspect ratio gives fairly good agreement between DNS and MES-IG reaction rates, the intricate details of highly complex void shapes impact hotspot characteristics to a significant extent. This work suggests possible improvements for the prediction of reaction rate in the energetic microstructure by adopting a more detailed description of shapes. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) has emerged as a promising approach to achieve energetic materials (EMs) with intricate geometries and controlled microstructures, which are crucial for safety and performance optimization. However, current AM methods still face limitations such as limited densities and inadequate solids loading. To overcome these limitations, we have developed a pressure‐assisted binder jet (PBJ) process that has the potential to allow for the fabrication of intricate EMs while preserving their desired properties. This study aims to investigate the effects of printing parameters on the microstructures and properties of EMs, including density, solids loading, mechanical properties, and heterogeneity. Our results demonstrate that the PBJ process achieves exceptional properties in EMs, including densities up to 83.4 % and solids loading up to 95.4 %, surpassing those achieved by existing AM processes. Furthermore, the mechanical properties of the fabricated EMs are comparable to those achieved using conventional fabrication techniques, including a compressive strength of 3.32 MPa, a Young's modulus of 16.68 MPa, a Poisson's ratio of 0.45, a shear modulus of 5.73 MPa, and a bulk modulus of 21.01 GPa. Various test cases were printed to showcase the ability of the PBJ process to create EMs with complex structures and exceptional properties. Micro‐computed tomography was employed to analyze the influence of printing parameters on the internal composition and microstructures of the printed specimens. [ABSTRACT FROM AUTHOR]

Material models for single-crystal β-HMX are systematically examined in the context of continuum pore-collapse simulations. Continuum predictions using five different isotropic material models are compared head-to-head with molecular dynamics (MD) predictions for a 50 nm cylindrical pore in β-HMX subject to a range of shock strengths. Shock waves were generated using a reverse-ballistic configuration, propagating along [010] in the MD simulations. The continuum models are improved hierarchically, drawing on temperature- and pressure-dependent MD-derived material parameters. This procedure reveals the sensitivity of the continuum predictions of pore collapse to the underlying thermophysical models. The study culminates in an MD-calibrated isotropic rate- and temperature-dependent strength model, which includes appropriate submodels for the temperature-dependent melting point of β-HMX [M. P. Kroonblawd and R. A. Austin, Mech. Mater. 152, 103644 (2021)], pressure-dependent shear modulus [A. Pereverzev and T. Sewell, Crystals 10, 1123 (2020)], and temperature-dependent specific heat, that produces continuum pore-collapse results similar to those predicted by MD. The resulting MD-informed model should improve the fidelity of simulations to predict the detonation initiation of HMX-based energetic materials containing micrometer-scale pores. [ABSTRACT FROM AUTHOR]

The sensitivity of an energetic material is strongly influenced by its microstructure. This work distinguishes the roles played by the microstructure (i.e., the meso-scale) in the macro-scale shock sensitivity of two different materials: TATB and HMX. To quantify sensitivity, we develop a meso-informed energy deposition model for a porous TATB material, following procedures from the previous work on HMX. Simulations of reactive void collapse in TATB are employed to calculate the rate of initiation and growth of hotspots. These rates are expressed as surrogate models, expressing meso-scale (hotspot) quantities of interest as functions of shock strength P s and void size D v o i d . The hotspot ignition and growth rate surrogates for TATB are compared with those for HMX, providing insights into meso-scale physics underlying shock sensitivity of these two energetic materials. The surrogate models are then used in a meso-informed ignition and growth (MES-IG) model to close macro-scale simulations of the shock response of porous TATB. We also obtain the run-to-detonation distances and generate Pop-plots to quantify macro-scale sensitivity. It is shown that Pop-plots for HMX-based energetic materials accord with behavior observed in experimental studies; however, there is a significant discrepancy between MES-IG predictions and experiments for TATB; the causes for this difference between HMX and TATB are discussed, pointing to areas for future work. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is rapidly emerging as a enabling tool for solving complex materials design problems. This paper aims to review recent advances in AI‐driven materials‐by‐design and their applications to energetic materials (EM). Trained with data from numerical simulations and/or physical experiments, AI models can assimilate trends and patterns within the design parameter space, identify optimal material designs (micro‐morphologies, combinations of materials in composites, etc.), and point to designs with superior/targeted property and performance metrics. We review approaches focusing on such capabilities with respect to the three main stages of materials‐by‐design, namely representation learning of microstructure morphology (i. e., shape descriptors), structure‐property‐performance (S−P−P) linkage estimation, and optimization/design exploration. We leave out "process" as much work remains to be done to establish the connectivity between process and structure. We provide a perspective view of these methods in terms of their potential, practicality, and efficacy towards the realization of materials‐by‐design. Specifically, methods in the literature are evaluated in terms of their capacity to learn from a small/limited number of data, computational complexity, generalizability/scalability to other material species and operating conditions, interpretability of the model predictions, and the burden of supervision/data annotation. Finally, we suggest a few promising future research directions for EM materials‐by‐design, such as meta‐learning, active learning, Bayesian learning, and semi‐/weakly‐supervised learning, to bridge the gap between machine learning research and EM research. [ABSTRACT FROM AUTHOR]

Predictive simulations of the shock‐to‐detonation transition (SDT) in heterogeneous energetic materials (EM) are vital to the design and control of their energy release and sensitivity. Due to the complexity of the thermo‐mechanics of EM during the SDT, both macro‐scale response and sub‐grid mesoscale energy localization must be captured accurately. This work proposes an efficient and accurate multiscale framework for SDT simulations of EM. We introduce a new approach for SDT simulation by using deep learning to model the mesoscale energy localization of shock‐initiated EM microstructures. The proposed multiscale modeling framework is divided into two stages. First, a physics‐aware recurrent convolutional neural network (PARC) is used to model the mesoscale energy localization of shock‐initiated heterogeneous EM microstructures. PARC is trained using direct numerical simulations (DNS) of hotspot ignition and growth within microstructures of pressed HMX material subjected to different input shock strengths. After training, PARC is employed to supply hotspot ignition and growth rates for macroscale SDT simulations. We show that PARC can play the role of a surrogate model in a multiscale simulation framework, while drastically reducing the computation cost and providing improved representations of the sub‐grid physics. The proposed multiscale modeling approach will provide a new tool for material scientists in designing high‐performance and safer energetic materials. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is rapidly emerging as a enabling tool for solving complex materials design problems. This paper aims to review recent advances in AI‐driven materials‐by‐design and their applications to energetic materials (EM). Trained with data from numerical simulations and/or physical experiments, AI models can assimilate trends and patterns within the design parameter space, identify optimal material designs (micro‐morphologies, combinations of materials in composites, etc.), and point to designs with superior/targeted property and performance metrics. We review approaches focusing on such capabilities with respect to the three main stages of materials‐by‐design, namely representation learning of microstructure morphology (i. e., shape descriptors), structure‐property‐performance (S−P−P) linkage estimation, and optimization/design exploration. We leave out "process" as much work remains to be done to establish the connectivity between process and structure. We provide a perspective view of these methods in terms of their potential, practicality, and efficacy towards the realization of materials‐by‐design. Specifically, methods in the literature are evaluated in terms of their capacity to learn from a small/limited number of data, computational complexity, generalizability/scalability to other material species and operating conditions, interpretability of the model predictions, and the burden of supervision/data annotation. Finally, we suggest a few promising future research directions for EM materials‐by‐design, such as meta‐learning, active learning, Bayesian learning, and semi‐/weakly‐supervised learning, to bridge the gap between machine learning research and EM research. [ABSTRACT FROM AUTHOR]

Predictive simulations of the shock‐to‐detonation transition (SDT) in heterogeneous energetic materials (EM) are vital to the design and control of their energy release and sensitivity. Due to the complexity of the thermo‐mechanics of EM during the SDT, both macro‐scale response and sub‐grid mesoscale energy localization must be captured accurately. This work proposes an efficient and accurate multiscale framework for SDT simulations of EM. We introduce a new approach for SDT simulation by using deep learning to model the mesoscale energy localization of shock‐initiated EM microstructures. The proposed multiscale modeling framework is divided into two stages. First, a physics‐aware recurrent convolutional neural network (PARC) is used to model the mesoscale energy localization of shock‐initiated heterogeneous EM microstructures. PARC is trained using direct numerical simulations (DNS) of hotspot ignition and growth within microstructures of pressed HMX material subjected to different input shock strengths. After training, PARC is employed to supply hotspot ignition and growth rates for macroscale SDT simulations. We show that PARC can play the role of a surrogate model in a multiscale simulation framework, while drastically reducing the computation cost and providing improved representations of the sub‐grid physics. The proposed multiscale modeling approach will provide a new tool for material scientists in designing high‐performance and safer energetic materials. [ABSTRACT FROM AUTHOR]

Predictive simulations of shock-to-detonation transitions (SDTs) of energetic materials must contend with uncertainties in the material properties, reactive models, and the microstructures of the material. In this work, we analyze the effects of uncertainties in the run-to-detonation distance h of a pressed energetic (HMX) material due to variabilities in the thermomechanical properties of HMX. The run distances are computed using a recently developed machine-learning based multiscale modeling framework, viz., the Meso-informed Ignition and Growth (MES-IG) model. The input uncertainties are first used in the MES-IG model to quantify the variabilities in the hotspot dynamics at the mesoscale. A Kriging-based Monte Carlo method is used to construct probability density functions (pdfs) for the mesoscale reaction-product formation rates; these are used to propagate the mesoscale uncertainties to the macroscale reaction-progress variables to construct pdfs for the run-to-detonation distance h. We evaluate uncertainties in h due to variabilities in six material properties, viz., specific heat, Grüneisen parameter, bulk modulus, yield strength, thermal expansion coefficient, and the thermal conductivity of the material. Among these six properties, h is found to be most sensitive to the variabilities in the specific heat of the material; the uncertainties in the specific heat amplify exponentially across scales and result in logarithmic pdfs for h. Thus, the paper not only quantifies and propagates uncertainties in material properties across scales in a multiscale model of SDT, but also ranks the properties with respect to the sensitivity of the SDT response of heterogeneous energetic materials on each property. [ABSTRACT FROM AUTHOR]

This study investigates the capabilities of a diffuse interface immersed boundary method in the simulation of fluid flow over an oscillating flat-plate airfoil at moderately high Reynolds numbers where interacting unsteady vortical flows control the flow behavior. The recently developed and modified Smoothed Profile Method (SPM) is adopted as a diffuse interface immersed boundary solver to model the fluid-structure interaction (FSI) problem. While in general diffuse interface methods are simpler to implement relative to sharp-interface methods (SIMs), they have been thought to lose accuracy in the simulation of moderate Reynolds number flows. The simulation results show that the lift coefficient and vorticity contours from SPM match well with experiments. Moreover, our detailed analysis of the vorticity fluxes at the airfoil leading edge and the comparison with experiments also show that SPM can be used for moderate Reynolds number external aerodynamics problems applicable to micro-air vehicles. [ABSTRACT FROM AUTHOR]

This paper studies the physics of shock compaction of metallic powder mixtures by focusing on two particles interacting under conditions of high compressive strain rates. Particle-particle interactions are simulated using a sharp interface Eulerian approach. Specifically, the present work seeks to understand how the disparity in bulk material properties such as yield strength and impedance leads to differences in spreading, heating, and contact area of the materials. In addition, the effects of interfacial phenomena such as friction and interfacial melting are examined using the sharp interface framework. The results show that the extent of interfacial contact between the materials is affected by material deformation and jetting. Of the material properties studied, the impedance mismatch exerts the highest influence on the heating of materials. Material yield strength also plays a significant role; the melting of materials is shown to play a minor role in the spreading and evolution of contact of the materials for the shock strengths tested. By focusing on two particles, this work provides insights elucidating the physics of shock compaction of powder mixtures, specifically the influence of bulk and interfacial properties on the extent of interfacial contact between dissimilar materials. [ABSTRACT FROM AUTHOR]

During compaction of large ensembles of metallic particles, the deformation and redistribution of the particles are strongly influenced by the bulk and interfacial properties of the interacting materials. Using a sharp-interface Eulerian approach, this paper studies the physics of shock compaction of clusters of particles by accurately capturing the interfacial dynamics of particle-particle interactions. The effect of disparity in bulk material properties such as impedance and yield strength and interfacial phenomena such as friction are quantified using interfacial area evolution as a measure of mixing of the materials. The results show that friction between the particles increases the mixing of the materials only for lower initial theoretical mean densities but does not significantly affect the overall dynamics of the compaction. The deformation and contact between the particles are most strongly influenced by the impedance mismatch of the materials. The difference in yield stress plays a minor role due to the predominance of pressure over deviatoric stresses. The results provide insights into the individual effects of impedance and yield stress mismatch between material pairs and also into the combined influence of mismatches in both properties. It is shown that apart from property mismatch, the average impedance of the mixture also plays a key role in mixing of the materials. [ABSTRACT FROM AUTHOR]

This work presents a multiscale modeling framework for predictive simulations of shock-to-detonation transition (SDT) in pressed energetic (HMX) materials. The macro-scale computations of SDT are performed using an ignition and growth (IG) model. However, unlike in the traditional semi-empirical ignition-and-growth model, which relies on empirical fits, in this work meso-scale void collapse simulations are used to supply the ignition and growth rates. This results in a macro-scale model which is sensitive to the meso-structure of the energetic material. Energy localization at the meso-scale due to hotspot ignition and growth is reflected in the shock response of the energetic material via surrogate models for ignition and growth rates. Ensembles of meso-scale reactive void collapse simulations are used to train the surrogate model using a Bayesian Kriging approach. This meso-informed Ignition and Growth (MES-IG) model is applied to perform SDT simulations of pressed HMXs with different porosity and void diameters. The computations are successfully validated against experimental pop-plots. Additionally, the critical energy for SDT is computed and the experimentally observed P s 2 τ s = constant relations are recovered using the MES-IG model. While the multiscale framework in this paper is applied in the context of an ignition-and-growth model, the overall surrogate model-based multiscale approach can be adapted to any macro-scale model for predicting SDT in heterogeneous energetic materials. [ABSTRACT FROM AUTHOR]

Crystals of energetic materials, such as 1,3,5,7-Tetranitro-1,3,5,7-tetrazocane (HMX), embedded in plastic binders are the building blocks of plastic-bonded explosives (PBX). Such heterogeneous energetic materials contain microstructural features such as sharp corners, interfaces between crystal and binder, intra- and extra-granular voids, and other defects. Energy localization or "hotspots" arise during shock interaction with the microstructural heterogeneities, leading to initiation of PBXs. In this paper, high-resolution numerical simulations are performed to elucidate the mechanistic details of shock-induced initiation in a PBX; we examine four different mechanisms: (1) shock-focusing at sharp corners or edges and its dependency on the shape of the crystal and the strength of the applied shock; (2) debonding between crystal and binder interfaces; (3) collapse of voids in the binder located near an HMX crystal; and (4) the collapse of voids within HMX crystals. Insights are obtained into the relative contributions of these mechanisms to the ignition and growth of hotspots. Understanding these mechanisms of energy localization and their relative importance for hotspot formation and initiation sensitivity of PBXs will aid in the design of energetic material-driven systems with controlled sensitivity, to prevent accidental initiation and ensure reliable performance. [ABSTRACT FROM AUTHOR]

Climate change mitigation demands rapid decarbonization; in cold regions such as the US Midwest, building thermal control (BTC) presents a potential for significant cuts in emissions by combining renewable energy with heat pumps. Trends in natural gas and electricity pricing over the period 2007–2020 have significantly upended economic feasibility of this decarbonization approach. Heat pump‐based BTC has moved from being cost advantageous in operations but higher GHG emitting (in 2007), to the exact opposite—higher cost but lower emitting (in 2020). Due to energy price fluctuations, the coefficient of performance required of a heat pump to breakeven on operational cost with high efficiency natural gas heating has doubled, changing from 2.25 to 4.5 over the period 2007–2019. The value of 4.5 is infeasible for all available cold‐climate heat pump systems. Thus, while electrification via both air‐source and ground‐source heat pumps is a commonly identified component of decarbonization plans in the region, actual implementation has declined. Improvements in heat pump performance, overcoming adoption barriers, and adopting policies that buffer long‐term climate solutions against short‐term price fluctuations are all needed. In part due to continuing decreases in carbon intensity of electricity, heat pump climate benefits can be substantial over project life cycles; however, there is no current policy mechanism to enable this benefit to be considered relative to short‐run economic considerations. [ABSTRACT FROM AUTHOR]

Shock‐induced collapse of an elongated pore in the energetic crystal β‐HMX (β‐1,3,5,7‐tetranitro‐1,3,5,7‐tetrazoctane) is examined using all‐atom molecular dynamics (MD) and continuum mechanics. The continuum simulation employs a recently proposed MD‐guided material model for β‐HMX. Collapse‐induced shear band formation and hotspot‐zone properties are calculated using both MD and continuum mechanics, for nearly identical simulation domains and identical impact conditions. The continuum model predicts shear band patterns and pore collapse behavior in good agreement with MD results; shear localization, plastic heating, and hydrodynamic impact‐generated temperature rise lead to geometrically complicated hotspot zones in the vicinity of the collapse site. This work demonstrates that—for the ≈10 GPa shock pressure studied—isotropic rate‐dependent Johnson‐Cook‐type elastoplastic models for HMX can provide physically consistent pore‐collapse dynamics and hotspot features in comparison to MD, for nontrivial pore shapes. Such physically accurate models are required for reliable predictions of detonation sensitivity and performance for shocked energetic crystals. Opportunities for further model improvement are identified. [ABSTRACT FROM AUTHOR]

For material modeling and discovery, synthetic microstructures play a critical role as digital twins. They provide stochastic samples upon which direct numerical simulations can be conducted to populate material databases. A large ensemble of simulation data on synthetic microstructures may provide supplemental data to inform and refine macroscopic material models, which might not be feasible from physical experiments alone. However, synthesizing realistic microstructures with realistic microstructural attributes is highly challenging. Thus, it is often oversimplified via rough approximations that may yield an inaccurate representation of the physical world. Here, we propose a novel deep learning method that can synthesize realistic three-dimensional microstructures with controlled structural properties using the combination of generative adversarial networks (GAN) and actor-critic (AC) reinforcement learning. The GAN-AC combination enables the generation of microstructures that not only resemble the appearances of real specimens but also yield user-defined physical quantities of interest (QoI). Our validation experiments confirm that the properties of synthetic microstructures generated by the GAN-AC framework are within a 5% error margin with respect to the target values. The scientific contribution of this paper resides in the novel design of the GAN-AC microstructure generator and the mathematical and algorithmic foundations therein. The proposed method will have a broad and substantive impact on the materials community by providing lenses for analyzing structure-property-performance linkages and for implementing the notion of 'materials-by-design'. [ABSTRACT FROM AUTHOR]

Shock load analysis of two different samples of pressed HMX energetic material is performed using the Eulerian compressible multimaterial code SCIMITAR3D. The numerical framework uses an image to computation approach to perform shock analysis on real microstructures of the energetic samples. Image processing algorithms are applied on SEM images of both samples to implicitly represent the microstructures using level set functions. The chemical decomposition of HMX is modeled using the Henson-Smilowitz multi-step kinetic mechanism. It is observed that microstructural characteristics play a crucial role in determining the ignition behavior of the energetic materials. For the applied shock loads and for the particular samples investigated, class III sample leads to initiation of chemical reaction and the class V sample does not ignite. It is also shown that the orientation of elongated voids with respect to incident shock load is an important factor contributing to the initiation of chemical reactions in the class III sample. This is explained by performing numerical experiments of elongated void oriented at different angles with respect to the shock load. Results show the importance of microstructural details, such as void size, distribution, and orientation for initiation. [ABSTRACT FROM AUTHOR]

The sensitivity of heterogeneous energetic (HE) materials (propellants, explosives, and pyrotechnics) is critically dependent on their microstructure. Initiation of chemical reactions occurs at hot spots due to energy localization at sites of porosities and other defects. Emerging multi-scale predictive models of HE response to loads account for the physics at the meso-scale, i.e. at the scale of statistically representative clusters of particles and other features in the microstructure. Meso-scale physics is infused in machine-learned closure models informed by resolved meso-scale simulations. Since microstructures are stochastic, ensembles of meso-scale simulations are required to quantify hot spot ignition and growth and to develop models for microstructure-dependent energy deposition rates. We propose utilizing generative adversarial networks (GAN) to spawn ensembles of synthetic heterogeneous energetic material microstructures. The method generates qualitatively and quantitatively realistic microstructures by learning from images of HE microstructures. We show that the proposed GAN method also permits the generation of new morphologies, where the porosity distribution can be controlled and spatially manipulated. Such control paves the way for the design of novel microstructures to engineer HE materials for targeted performance in a materials-by-design framework. [ABSTRACT FROM AUTHOR]

Morphology and dynamics at the mesoscale play crucial roles in the overall macro- or system-scale flow of heterogeneous materials. In a multi-scale framework, closure models upscale unresolved sub-grid (mesoscale) physics and therefore encapsulate structure–property (S–P) linkages to predict performance at the macroscale. This work establishes a route to S–P linkage, proceeding all the way from imaged microstructures to flow computations in one unified level-set-based framework. Level sets are used to: (1) define embedded geometries via image segmentation; (2) simulate the interaction of sharp immersed boundaries with the flow field; and (3) calculate morphological metrics to quantify structure. Mesoscale dynamics is computed to calculate sub-grid properties, i.e., closure models for momentum and energy equations. The S–P linkage is demonstrated for two types of multi-material flows: interaction of shocks with a cloud of particles and reactive meso-mechanics of pressed energetic materials. We also present an approach to connect local morphological characteristics in a microstructure containing topologically complex features with the shock response of imaged samples of such materials. This paves the way for using geometric machine learning techniques to associate imaged morphologies with their properties. [ABSTRACT FROM AUTHOR]

Surrogate models for hotspot ignition and growth rates were presented in Part I (Nassar et al., Shock Waves 29(4):537–558, 2018), where the hotspots were formed by the collapse of single cylindrical voids. Such isolated cylindrical voids are idealizations of the void morphology in real meso-structures. This paper therefore investigates the effect of non-cylindrical void shapes and void–void interactions on hotspot ignition and growth. Surrogate models capturing these effects are constructed using a Bayesian Kriging approach. The training data for machine learning the surrogates are derived from reactive void collapse simulations spanning the parameter space of void aspect ratio, void orientation (θ) , and void fraction (ϕ) . The resulting surrogate models portray strong dependence of the ignition and growth rates on void aspect ratio and orientation, particularly when they are oriented at acute angles with respect to the imposed shock. The surrogate models for void interaction effects show significant changes in hotspot ignition and growth rates as the void fraction increases. The paper elucidates the physics of hotspot evolution in void fields due to the creation and interaction of multiple hotspots. The results from this work will be useful not only for constructing meso-informed macroscale models of HMX, but also for understanding the physics of void–void interactions and sensitivity due to void shape and orientation. [ABSTRACT FROM AUTHOR]

All‐atom molecular dynamics (MD) and Eulerian continuum simulations, performed using the LAMMPS and SCIMITAR3D codes, respectively, were used to study thermo‐mechanical aspects of the shock‐induced collapse of an initially cylindrical 50 nm diameter pore in single crystals of 1,3,5‐triamino‐2,4,6‐trinitrobenzene (TATB). Three impact speeds, 0.5 km s−1, 1.0 km s−1 and 2.0 km s−1, were used to generate the shocks. These impact conditions are expected to yield collapse mechanisms ranging from predominantly visco‐plastic to hydrodynamic. For the MD studies, three crystal orientations (relative to shock‐propagation direction) were examined that span the limiting cases with respect to the crystalline anisotropy in TATB. An isotropic constitutive model was used for the continuum simulations, thus crystal anisotropy is absent. The evolution of spatiotemporally resolved quantities during collapse is reported including local pressure, temperature, pore size and shape, and material flow. Multiple models for the melting curve and specific heat were studied. Within the isotropic elastic/perfectly plastic continuum framework and for the range of impact conditions studied, the specific heat and melting curve sub‐models are shown to have modest effects on the continuum hotspot predictions in the present inert calculation. Treating the MD predictions as 'ground truth', albeit with a classical rather than quantum‐like heat capacity, it is clear that – at a minimum – an extension of the constitutive model to account for crystal plasticity and anisotropic strength will be required for a high‐fidelity continuum description. [ABSTRACT FROM AUTHOR]

This work presents the procedure for constructing a machine-learned surrogate model for hot-spot ignition and growth rates in pressed HMX materials. A Bayesian kriging algorithm is used to assimilate input data obtained from high-resolution mesoscale simulations. The surrogates are built by generating a sparse set of training data using reactive mesoscale simulations of void collapse by varying loading conditions and void sizes. Insights into the physics of void collapse and ignition and growth of hot spots are obtained. The criticality envelope for hot spots is obtained as the function Σ cr = f P s , D void where P s is the imposed shock pressure and D void is the void size. Criticality of hot spots is classified into the plastic collapse and hydrodynamic jetting regimes. The information obtained from the surrogate models for hot-spot ignition and growth rates and the criticality envelope can be utilized in meso-informed ignition and growth models to perform multi-scale simulations of pressed HMX materials. [ABSTRACT FROM AUTHOR]

The formation of hotspots due to collapse of voids leads to enhanced sensitivity of heterogeneous energetic (HE) materials. Several mechanisms of void collapse have been identified, but the regimes in which these mechanisms dominate have not been clearly delineated using scaling arguments and dimensionless parameters. This paper examines void collapse in cyclotetramethylene-tetranitramine (HMX) to demarcate regimes where plastic collapse and hydrodynamic jetting play dominant roles in influencing hotspot related sensitivity. Using scaling arguments, a criticality envelope for HMX is derived in the form Σ c r = ∑ ( P s , D v o i d ) , i.e., as a function of shock pressure Ps and void size Dvoid, which are controllable design parameters. Once a critical hotspot forms, its subsequent growth displays a complex relationship to Ps and Dvoid. These complexities are explained with scaling arguments that clarify the physical mechanisms that predominate in various regimes of hotspot formation. The insights and scaling laws obtained can be useful in the design of HE materials. [ABSTRACT FROM AUTHOR]

Summary: This work presents a level‐set–based sharp interface technique to simulate the evolution of damage in ductile materials under high velocity impact conditions. The level‐set method is adopted to track all interfaces including damage zones within the materials. Two types of damage are considered, ie, the creation of spall zones due to damage accumulation in homogeneous ductile materials and interfacial debonding in heterogeneous materials. Spall is simulated using continuum damage models and a level‐set–based crack generation and evolution algorithm. Three continuum damage models are tested for metal targets subjected to flyer impact; the results from the current code (SCIMITAR3D) are compared with the two widely used computer codes EPIC and CTH, and to experimental data; it is found that the computer codes are in good agreement among each other, but agreement of all methods with experimental data is not uniform. At material interfaces, damage is handled using a cohesive zone model and evolving level sets to create void spaces because of material separation due to debonding. Finally, ductile damage combined with debonding is simulated in an Al‐Ni laminate impacted by a projectile. The results demonstrate the ability of the present approach to simulate various types of damage in materials with heterogeneities and inclusions. [ABSTRACT FROM AUTHOR]

Macroscale models of shock-particle interactions require closure terms for unresolved solid-fluid momentum and energy transfer. These comprise the effects of mean as well as fluctuating fluid-phase velocity fields in the particle cloud. Mean drag and Reynolds stress equivalent terms (also known as pseudo-turbulent terms) appear in the macroscale equations. Closure laws for the pseudo-turbulent terms are constructed in this work from ensembles of high-fidelity mesoscale simulations. The computations are performed over a wide range of Mach numbers (M) and particle volume fractions (ϕ) and are used to explicitly compute the pseudo-turbulent stresses from the Favre average of the velocity fluctuations in the flow field. The computed stresses are then used as inputs to a Modified Bayesian Kriging method to generate surrogate models. The surrogates can be used as closure models for the pseudo-turbulent terms in macroscale computations of shock-particle interactions. It is found that the kinetic energy associated with the velocity fluctuations is comparable to that of the mean flow—especially for increasing M and ϕ. This work is a first attempt to quantify and evaluate the effect of velocity fluctuations for problems of shock-particle interactions. [ABSTRACT FROM AUTHOR]

A Cartesian grid-based sharp interface method is presented for viscous simulations of shocked particle-laden flows. The moving solid–fluid interfaces are represented using level sets. A moving least-squares reconstruction is developed to apply the no-slip boundary condition at solid–fluid interfaces and to supply viscous stresses to the fluid. The algorithms developed in this paper are benchmarked against similarity solutions for the boundary layer over a fixed flat plate and against numerical solutions for moving interface problems such as shock-induced lift-off of a cylinder in a channel. The framework is extended to 3D and applied to calculate low Reynolds number steady supersonic flow over a sphere. Viscous simulation of the interaction of a particle cloud with an incident planar shock is demonstrated; the average drag on the particles and the vorticity field in the cloud are compared to the inviscid case to elucidate the effects of viscosity on momentum transfer between the particle and fluid phases. The methods developed will be useful for obtaining accurate momentum and heat transfer closure models for macro-scale shocked particulate flow applications such as blast waves and dust explosions. [ABSTRACT FROM PUBLISHER]

A Cartesian grid solver is developed that is capable of simulating the convection-dominated melting processes in a latent-heat thermal storage device (TSD). The Navier-Stokes equations are solved using a dynamically refined mesh. The phase boundary is tracked using the enthalpy method. Conjugate heat transfer is calculated with a strongly coupled implicit scheme. The approach does not require the creation of a geometry-specific grid, and so allows for efficient prototyping of different complex geometric designs. Systematic benchmarking of the results against other numerical approaches is conducted, followed by tests of two basic prototypes for the design of a TSD. [ABSTRACT FROM PUBLISHER]

The flow dynamics through the peripheral and hinge regions of a bi-leaflet mechanical heart valve are complex and result in abnormally high shear stresses particularly during the closing phase of the valve function. It has been observed that the late stages of closure are more significant in the dynamics of platelet activation; therefore, the later stages of closure are simulated by solving the two-dimensional Navier–Stokes equations using an Eulerian Levelset-based sharp interface Cartesian grid method. Using a fixed Cartesian mesh incorporating local mesh refinement for solution accuracy and efficiency, the flow-through within a recessed hinge design and an open pivot hinge design is compared. Platelets are modeled as point particles by Lagrangian particle tracking algorithm with one way coupling. A dilute particle flow is assumed and particle–particle interactions are neglected. It was observed that the hinge region of the open pivot valve indicated a lower potential for activation of platelets compared to that in valves with a recessed hinge design. [ABSTRACT FROM AUTHOR]

Sharp interface Cartesian grid methods are capable of simulating complex moving boundary problems on fixed meshes while treating embedded interfaces accurately. This article further enhances the effectiveness of the sharp interface method by devising techniques for adaptive mesh resolution combined with parallel processing. These extensions enable dealing with problems involving disparate length scales encountered in many applications. A tree-based adaptive local mesh refinement scheme is developed to complement the sharp interface Cartesian grid method for efficient and optimised calculations. Detailed timing and accuracy data are presented for a variety of benchmark problems involving moving boundaries. Guidelines for selecting mesh refinement criteria for moving boundary calculations are developed. Issues associated with parallelisation of the overall framework are tackled. The capabilities of the method are demonstrated in a number of moving boundary problems, which require adequate resolution of a wide range of length scales and three-dimensional flows. [ABSTRACT FROM AUTHOR]

We present and validate a numerical technique for computing dendritic growth of crystals from pure melts. The solidification process is computed in the diffusion-driven limit. The governing equations are solved on a fixed Cartesian mesh and a mixed Eulerian-Lagrangian framework is used to treat the immersed phase boundary as a sharp solid-fluid interface. A conservative finite-volume discretization is employed which allows the boundary conditions to be applied exactly at the moving surface. The results from our calculations are compared with two-dimensional microscopic solvability theory. It is shown that the method predicts dendrite tip characteristics in good agreement with the theory. The sharp interface treatment allows discontinuous material property variation at the solid-liquid interface. Calculations with such discontinuities are also shown to produce results in agreement with solvability and with other sharp interface simulations. [ABSTRACT FROM AUTHOR]

A three-layer Newtonian model is investigated using a combined Eulerian-Lagrangian computational method to describe the dynamic behaviour of leukocytes. The model, composed of a cell membrane (outer layer), cytoplasm (middle layer) and nucleus (inner layer), can better describe the recovery characteristics because large viscosity and capillarity differences between layers are considered, and both Newtonian and seemingly non-Newtonian behaviours reported in the literature can be reproduced. It is found that, to describe adequately the various rheological characteristics of leukocytes, the presence of the highly viscous nucleus and its deformation/recovery, as well as the surface energy stored in the fluid interfaces, are critical. Photographs from pipette experiments using a fluorescent technique confirm the theoretical finding of the important role played by the nucleus in cell deformation. [ABSTRACT FROM AUTHOR]

For many materials processing techniques, the meniscus of liquid bridging the crystal to the melt is critical in determining the properties of the solidified crystal. It is standard practice for existing theoretical models to use equilibrium meniscus shapes with specified contact angle to represent the behavior of the meniscus. It is shown here that with such boundary conditions, multiple solutions exist to the axisymmetric form of the Laplace–Young equation. Furthermore, these possible meniscus profiles may, depending on the interaction of Bond number, pressurization, aspect ratio and contact angle, correspond to minima, maxima or nonextrema points, as far as energy is concerned. The implications of this observation on meniscus stability are explored. The effect of direction of pulling in relation to gravity is also investigated. It appears that for tall menisci, commonly adopted equilibrium shapes may be unstable and the consequent dynamic behavior must be considered. Quasiequilibrium dynamics of the meniscus is simulated using a simplified hysteresis model for the contact angle at the top of the meniscus. A variety of behavior is found to arise, which is not fully captured by relations governing meniscus behavior used hitherto in many theoretical simulations. [ABSTRACT FROM AUTHOR]