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DOE OSTI2018

Multidimensional Numerical Simulations of Knocking Combustion in a Cooperative Fuel Research Engine

A numerical approach was developed based on multidimensional computational fluid dynamics (CFD) to predict knocking combustion in a cooperative fuel research (CFR) engine. G-equation model was employed to track the turbulent flame front and a multizone model was used to capture auto-ignition in the end-gas. Furthermore, a novel methodology was developed wherein a lookup table generated from a chemical kinetic mechanism could be employed to provide laminar flame speed as an input to the Gequation model, instead of using empirical correlations. To account for fuel chemistry effects accurately and lower the computational cost, a compact 121-species primary reference fuel (PRF) skeletal mechanism was developed from a detailed gasoline surrogate mechanism using the directed relation graph (DRG) assisted sensitivity analysis (DRGASA) reduction technique. Extensive validation of the skeletal mechanism was performed against experimental data available from the literature on both homogeneous ignition delay and laminar flame speed. The skeletal mechanism was used to generate lookup tables for laminar flame speed as a function of pressure, temperature, and equivalence ratio. The numerical model incorporating the skeletal mechanism was employed to perform simulations under research octane number (RON) and motor octane number (MON) conditions for two different PRFs. Parametric tests were conducted at different compression ratios (CR) and the predicted values of critical CR, delineating the boundary between “no knock” and “knock,” were found to be in good agreement with available experimental data. The virtual CFR engine model was, therefore, demonstrated to be capable of adequately capturing the sensitivity of knock propensity to fuel chemistry.

CFD↗

DOE OSTI2016

Analysis of the LSC microbunching instability in MaRIE linac reference design

In this report we estimate the effect of the microbunching instability in the MaRIE XFEL linac. The reference design for the linac is described in a separate report. The parameters of the L1, L2, and L3 linacs as well as BC1 and BC2 bunch compressors were the same as in the referenced report. The beam dynamics was assumed to be linear along the accelerator (which is a reasonable assumption for estimating the effect of the microbunching instability). The parameters of the bunch also match the parameters described in the referenced report. Additionally, it was assumed that the beam radius is equal to R = 100 m and does not change along linac. This assumption needs to be revisited at later studies. The beam dynamics during acceleration was accounted in the matrix formalism using a Matlab code. The input parameters for the linacs are: RF peak gradient, RF frequency, RF phase, linac length, and initial beam energy. The energy gain and the imposed chirp are calculated based on the RF parameters self-consistently. The bunch compressors are accounted in the matrix formalism as well. Each chicane is characterized by the beam energy and the R56 matrix element. It was confirmed that the linac and beam parameters described previously provide two-stage bunch compression with compression ratios of 10 and 20 resulting in the bunch of 3kA peak current.

43 PARTICLE ACCELERATORS↗

DOE OSTI2023

zPerf: A Statistical Gray-Box Approach to Performance Modeling and Extrapolation for Scientific Lossy Compression

With the scaling up of simulation-based scientific discovery on high-performance computing systems, the disparity between compute and I/O has increased, forcing domain scientists to save only a small amount of simulation data to persistent storage. This can result in the loss of essential physics fields that are needed for data analysis. While error-bounded lossy compression has made tremendous progress in bridging the gap between compute and I/O, the lack of understanding of compression performance remains a key hurdle to its wide adoption. Here, in this work, we present zPerf, a statistical gray-box performance modeling approach for scientific lossy compression. Our contributions are threefold: 1) We develop zPerf to estimate the performance of lossy compression techniques, based on in-depth understanding and statistical modeling for data features and core compression metrics; 2) We demonstrate the in-detailed implementation of zPerf using two case studies, where we derive the performance modeling for SZ and ZFP, two leading lossy compressors; 3) We evaluate the effectiveness of zPerf on real-world datasets across various domains. Based on the evaluation, we demonstrate the efficacy of the zPerf performance model; 4) We further discuss three case studies where zPerf is applied to extrapolate the compression ratio of SZ and ZFP with alternative encoding schemes as well as ZFP with an alternative transform scheme. Through the case studies, we demonstrate the potential of zPerf for exploring the design space of lossy compression, which has hardly been studied in the literature.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Fast 2D Bicephalous Convolutional Autoencoder for Compressing 3D Time Projection Chamber Data

High-energy large-scale particle colliders produce data at high speed in the order of 1 terabytes per second in nuclear physics and petabytes per second in high energy physics. Developing real-time data compression algorithms to reduce such data at high throughput to fit permanent storage has drawn increasing attention. Specifically, at the newly constructed sPHENIX experiment at the Relativistic Heavy Ion Collider (RHIC), a time projection chamber is used as the main tracking detector, which records particle trajectories in a volume of three-dimensional (3D) cylinder. The resulting data are usually very sparse with occupancy around 10.8%. Such sparsity presents a challenge to conventional learning-free lossy compression algorithms, such as SZ, ZFP, and MGARD. The 3D convolutional neural network (CNN)-based approach, Bicephalous Convolutional Autoencoder (BCAE), outperforms traditional methods both in compression rate and reconstruction accuracy. BCAE can also utilize the computation power of graphical processing units suitable for deployment in a modern heterogeneous highperformance computing environment. This work introduces two BCAE variants: BCAE++ and BCAE-2D. BCAE++ achieves a 15% better compression ratio and a 77% better reconstruction accuracy measured in mean absolute error compared with BCAE. BCAE-2D treats the radial direction as the channel dimension of an image, resulting in a 3× speedup in compression throughput. In addition, we demonstrate an unbalanced autoencoder with a larger decoder can improve reconstruction accuracy without significantly sacrificing throughput. Lastly, we observe both the BCAE++ and BCAE-2D can benefit more from using half-precision mode in throughput (76 - 79% increase) without loss in reconstruction accuracy. The source code and links to data and pretrained models can be found at https://github.com/BNL-DAQ-LDRD/NeuralCompression_v2

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2020

Performance Optimization for Relative-Error-Bounded Lossy Compression on Scientific Data

Scientific simulations in high-performance computing (HPC) environments generate vast volume of data, which may cause a severe I/O bottleneck at runtime and a huge burden on storage space for postanalysis. Unlike traditional data reduction schemes such as deduplication or lossless compression, not only can error-controlled lossy compression significantly reduce the data size but it also holds the promise to satisfy user demand on error control. Pointwise relative error bounds (i.e., compression errors depends on the data values) are widely used by many scientific applications with lossy compression since error control can adapt to the error bound in the dataset automatically. Pointwise relative-error-bounded compression is complicated and time consuming. In this article, we develop efficient precomputation-based mechanisms based on the SZ lossy compression framework. Our mechanisms can avoid costly logarithmic transformation and identify quantization factor values via a fast table lookup, greatly accelerating the relative-error-bounded compression with excellent compression ratios. In addition, we reduce traversing operations for Huffman decoding, significantly accelerating the decompression process in SZ. Experiments with eight well-known real-world scientific simulation datasets show that our solution can improve the compression and decompression rates (i.e., the speed) by about 40 and 80 p, respectively, in most of cases, making our designed lossy compression strategy the best-in-class solution in most cases.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

DOE OSTI2021

A stable corridor for toroidal plasma compression

In this study, a toroidal plasma compressed by a collapsing flux conserver is analyzed to reveal stable scenarios of operation to high compression ratios. The resistive and ideal MHD stability is calculated in full toroidal geometry, using the asymptotic matching method in realistic conditions, and comparing with nonlinear simulations. The near edge current profile, controlled by toroidal field ramping during compression, is shown to be critical to stability due to coupling between poloidal components of the least stable mode. The extension of a length of shaft on axis is also found to be critical at high compression, as the resulting good curvature region in magnetic field stabilizes pressure driven modes that would otherwise be unstable. This work extends from previous studies, which initially showed the existence of a stable scenario, to include findings of more extensive stable zones, detailed effects of geometry, and nonlinear simulations of the instabilities. The nonlinear simulations of the compression are consistent with the linear analyses, confirming both the conservation and stability properties.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2016

Novel Data Reduction Based on Statistical Similarity

Applications such as scientific simulations and power grid monitoring are generating so much data quickly that compression is essential to reduce storage requirement or transmission capacity. To achieve better compression, one is often willing to discard some repeated information. These lossy compression methods are primarily designed to minimize the Euclidean distance between the original data and the compressed data. But this measure of distance severely limits either reconstruction quality or compression performance. In this paper, we propose a new class of compression method by redefining the distance measure with a statistical concept known as exchangeability. This approach reduces the storage requirement and captures essential features, while reducing the storage requirement. In this paper, we report our design and implementation of such a compression method named IDEALEM. To demonstrate its effectiveness, we apply it on a set of power grid monitoring data, and show that it can reduce the volume of data much more than the best known compression method while maintaining the quality of the compressed data. Finally, in these tests, IDEALEM captures extraordinary events in the data, while its compression ratios can far exceed 100.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Maintaining Trust in Reduction: Preserving the Accuracy of Quantities of Interest for Lossy Compression

As the growth of data sizes continues to outpace computational resources, there is a pressing need for data reduction techniques that can significantly reduce the amount of data and quantify the error incurred in compression. Compressing scientific data presents many challenges for reduction techniques since it is often on non-uniform or unstructured meshes, is from a high-dimensional space, and has many Quantities of Interests (QoIs) that need to be preserved. To illustrate these challenges, we focus on data from a large scale fusion code, XGC. XGC uses a Particle-In-Cell (PIC) technique which generates hundreds of PetaBytes (PBs) of data a day, from thousands of timesteps. XGC uses an unstructured mesh, and needs to compute many QoIs from the raw data, f.One critical aspect of the reduction is that we need to ensure that QoIs derived from the data (density, temperature, flux surface averaged momentums, etc.) maintain a relative high accuracy. We show that by compressing XGC data on the high-dimensional, nonuniform grid on which the data is defined, and adaptively quantizing the decomposed coefficients based on the characteristics of the QoIs, the compression ratios at various error tolerances obtained using a multilevel compressor (MGARD) increases more than ten times. We then present how to mathematically guarantee that the accuracy of the QoIs computed from the reduced f is preserved during the compression. We show that the error in the XGC density can be kept under a user-specified tolerance over 1000 timesteps of simulation using the mathematical QoI error control theory of MGARD, whereas traditional error control on the data to be reduced does not guarantee the accuracy of the QoIs.

Gong, Qian↗

DOE OSTI2021

Numerical Analysis of Fuel Effects on Advanced Compression Ignition Using a Cooperative Fuel Research Engine Computational Fluid Dynamics Model

Growing environmental concerns and demand for a better fuel economy are driving forces that motivate the research for more advanced engines. Multi-mode combustion strategies have gained attention for their potential to provide high thermal efficiency and low emissions for light-duty applications. These strategies target optimizing the engine performance by correlating different combustion modes to load operating conditions. The extension from boosted spark ignition (SI) mode at high loads to advanced compression ignition (ACI) mode at low loads can be achieved by increasing the compression ratio and utilizing intake air heating. Further, in order to enable an accurate control of intake charge condition for ACI mode and rapid mode-switches, it is essential to gain fundamental insights into the autoignition process. Within the scope of ACI, homogeneous charge compression ignition (HCCI) mode is of significant interest. It is known for its potential benefits, operation at low fuel consumption, low NOx, and particulate matter (PM) emissions. In the present work, a virtual Cooperative Fuel Research (CFR) engine model is used to analyze fuel effects on ACI combustion. In particular, the effect of fuel octane sensitivity (S) (at constant Research Octane Number (RON)) on autoignition propensity is assessed under beyond-RON (BRON) and beyond-MON (BMON) ACI conditions. The three-dimensional CFR engine computational fluid dynamics (CFD) model employs a finite-rate chemistry approach with a multi-zone binning strategy to capture autoignition. Two binary blends with Research Octane Number (RON) of 90 are chosen for this study: primary reference fuel (PRF) with S = 0 and toluene–heptane (TH) blend with S = 10.8, representing paraffinic and aromatic gasoline surrogates. Reduced mechanisms for these blends are generated from a detailed gasoline surrogate kinetic mechanism. Simulation results with the reduced mechanisms are validated against experimental data from an in-house CFR engine, with respect to in-cylinder pressure, heat release rate, and combustion phasing. Thereafter, the sensitivity of combustion behavior to ACI operating condition (BRON versus BMON), air-fuel ratio (λ = 2 and 3), and engine speed (600 and 900 rpm) is analyzed for both fuels. It is shown that the sensitivity of a fuel’s autoignition characteristics to λ and engine speed significantly differs at BRON and BMON conditions. Moreover, this sensitivity is found to vary among fuels, despite the same RON. It is also observed that the presence of low-temperature heat release (LTHR) under BRON condition leads to more sequential autoignition and longer combustion duration than BMON condition. Finally, the study indicates that the octane index (OI) fails to capture the trend in the variation of autoignition propensity with S under the BMON condition.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2020

Stable heat and particle flux detachment with efficient particle exhaust in the island divertor of Wendelstein 7-X

The island divertor concept is an innovative and promising idea to handle heat and particle exhaust in stellarators. At the Wendelstein 7-X (W7-X) stellarator, this divertor concept plays a central role in the device mission to demonstrate reactor relevant plasma confinement for steady-state time scales of up to 30 minutes in the high-performance campaign (OP2) starting in 2022. During the recently concluded first campaign with the inertially cooled island divertor, a large step in the experimental qualification of this divertor concept has been made. In discharges heated with electron cylotron resonance heating of 5-6 MW, central densities in the range of 0.7-1.2 × 10 20 m -3 have been reached in combination with full divertor heat flux detachment. Also, significant neutral gas pressures and neutral compression ratios were shown for the first time in combination with reduced divertor particle flux. The divertor heat loads drop by an order of magnitude from >5 MW m -2 to below 0.5 MW m-2 with increasing density, and substantial compression of neutrals reaching neutral pressure in the sub-divertor volume of >6.0 × 10 -4 mbar was seen. These elevated neutral pressure levels can be obtained and maintained with an up to 80% reduction of the particle fluxes onto the divertor target tiles. This discharge scenario was held stably detached for up to 28 seconds, which is equivalent to several hundred energy confinement times τ E and longer than the time scales for current relaxation. No impurity accumulation was seen at constant Z eff ≈ 1.5 and the stored energy stayed constant at levels of W dia >600 kJ. The level of neutral pressure and compression reached in this scenario extrapolates well to the steady-state particle exhaust requirements for high-performance steady-state operation in OP2, in which the fully actively cooled high-heat-flux divertor will be available. Finally, an overview of this recently discovered divertor regime is given and the status of the physics understanding based on modeling of these regimes with the EMC3-EIRENE code is presented.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2019

Accelerating Relative-error Bounded Lossy Compression for HPC datasets with Precomputation-Based Mechanisms

Scientific simulations in high-performance computing (HPC) environments are producing vast volume of data, which may cause a severe I/O bottleneck at runtime and a huge burden on storage space for post-analysis. Unlike the traditional data reduction schemes (such as deduplication or lossless compression), not only can error-controlled lossy compression significantly reduce the data size but it can also hold the promise to satisfy user demand on error control. Point-wise relative error bounds (i.e., compression errors depends on the data values) are widely used by many scientific applications in the lossy compression, since error control can adapt to the precision in the dataset automatically. Point-wise relative error bounded compression is complicated and time consuming. In this work, we develop efficient precomputation-based mechanisms in the SZ lossy compression framework. Our mechanisms can avoid costly logarithmic transformation and identify quantization factor values via a fast table lookup, greatly accelerating the relative-error bounded compression with excellent compression ratios. In addition, our mechanisms also help reduce traversing operations for Huffman decoding, and thus significantly accelerate the decompression process in SZ. Experiments with four well-known real-world scientific simulation datasets show that our solution can improve the compression rate by about 30% and decompression rate by about 70% in most of cases, making our designed lossy compression strategy the best choice in class in most cases.

compression rate↗

DOE OSTI2019

Ignition, Flame Progation, and End-Gas Autoignition Studies of Natural Gas/ EGR Blends in a Rapid Compression Machine

The development of high efficiency, spark ignited natural gas engines is currently limited by engine knock at high power density (at high compression ratio and/or elevated boost pressures) and misfire (at lean conditions and/or high exhaust gas recirculation levels). The knock and misfire limits are further confounded by the wide variety in fuel reactivity observed in “pipeline quality” natural gas. In this study, a rapid compression machine (RCM) was used to characterize the effects of variation in natural gas fuel reactivity and exhaust gas recirculation (EGR) on homogeneous ignition delay, flame propagation rate and end-gas autoignition (EGAI) propensity for stoichiometric natural gas/oxidizer/EGR blends. Pipeline quality natural gas with variable reactivity (68 < Methane Number < 95) was simulated using mixtures of methane, ethane, and propane. EGR was simulated with mixtures of Ar, CO2, CO, and NO. EGR substitution rates were varied from 0 to 30 mass percent. Ignition delay period under homogeneous autoignition conditions was measured at compressed pressures of 24.0 to 33.1 bar and compressed temperatures of 667 to 980 K. Flame propagation rate and EGAI propensity was measured at spark-ignition pressures of 29.9 to 32.9 bar and temperatures of 721 to 782 K.

Mohr, Jeffrey↗

DOE OSTI2019

Ignition, Flame Propagation, and End-Gas Autoignition Studies of Natural Gas/EGR Blends in a Rapid Compression Machine

Ignition Delay, Flame Propagation Rate, Rapid Comp↗

DOE OSTI2019

End-Gas Autoignition Fraction and Flame Propagation Rate in Laser-Ignited Primary Reference Fuel Mixtures at Elevated Temperature and Pressure

Engine knock in spark-ignited (SI) engines is initiated by autoignition and detonation in the unburned gases (i.e., end-gas) upstream of the spark-ignited, propagating, turbulent premixed flame. Knock propensity of fuel/air mixtures is quantified by research octane number (RON), motor octane number (MON), or methane number (MN; for gaseous fuels), which are typically measured using single-cylinder, variable compression ratio research engines. In this study, we demonstrate the ability to evaluate knock propensity of SI fuels via observations of end-gas autoignition (EGAI) in unburned gases upstream of laser-ignited, premixed flames at elevated pressures and temperatures in a rapid compression machine. Stoichiometric primary reference fuel (PRF; n-heptane/isooctane) blends of varying reactivity (50 ≤ PRF ≤ 100) were ignited using an Nd:YAG laser at five discrete temperature and pressure conditions, all in excess of 485 K and 16.75 bar. Laser ignition produced outwardly-propagating, laminar premixed flames. High-speed pressure measurements, paired with high-speed Schlieren imaging, clearly indicated the presence of EGAI. The fraction of the total heat release attributed to EGAI (e.g. EGAI fraction) varied inversely with octane number. EGAI fraction and flame propagation rate were both influenced by the magnitude of convection in the unburned gases upstream of the propagating flame caused by variation in piston timing. Experiments were accompanied by three-dimensional computational modeling with detailed chemical kinetics performed using CONVERGETM. The model results reveal low-temperature heat release and hydrogen peroxide formation in the end-gas upstream of the propagating laminar flame. Low-temperature heat release increases the temperature and degree of chain branching in the end-gas and ultimately leads to EGAI.

End-Gas Autoignition, Engine Knock, Rapid Compress↗

DOE OSTI2014

Towards efficient backward-in-time adjoint computations using data compression techniques

In the context of a posteriori error estimation for nonlinear time-dependent partial differential equations, the state-of-the-practice is to use adjoint approaches which require the solution of a backward-in-time problem defined by a linearization of the forward problem. One of the major obstacles in the practical application of these approaches, we found, is the need to store, or recompute, the forward solution to define the adjoint problem and to evaluate the error representation. Our study considers the use of data compression techniques to approximate forward solutions employed in the backward-in-time integration. The development derives an error representation that accounts for the difference between the standard-approach and the compressed approximation of the forward solution. This representation is algorithmically similar to the standard representation and only requires the computation of the quantity of interest for the forward solution and the data-compressed reconstructed solution (i.e. scalar quantities that can be evaluated as the forward problem is integrated). This approach is then compared with existing techniques, such as checkpointing and time-averaged adjoints. Lastly, we provide numerical results indicating the potential efficiency of our approach on a transient diffusion–reaction equation and on the Navier–Stokes equations. These results demonstrate memory compression ratios up to 450×450× while maintaining reasonable accuracy in the error-estimates.

42 ENGINEERING↗

DOE OSTI2020

Towards Feature-Preserving 2D and 3D Vector Field Compression

The objective of this work is to develop error-bounded lossy compression methods to preserve topological features in 2D and 3D vector fields. Specifically, we explore the preservation of critical points in piecewise linear vector fields. We define the preservation of critical points as, without any false positive, false negative, or false type change in the decompressed data, (1) keeping each critical point in its original cell and (2) retaining the type of each critical point (e.g., saddle and attracting node). The key to our method is to adapt a vertex-wise error bound for each grid point and to compress input data together with the error bound field using a modified lossy compressor. Our compression algorithm can be also embarrassingly parallelized for large data handling and in situ processing. We benchmark our method by comparing it with existing lossy compressors in terms of false positive/negative/type rates, compression ratio, and various vector field visualizations with several scientific applications.

critical points↗

DOE OSTI2021

Explore the Potential to Formulate Feedstock Blends from Diverse Biomass inputs for Improved Processing Performance at Lower Costs

This work aims to understand the pelleting characteristics of 2-inch top-pine residue blended with switchgrass at high moisture content. The process variables tested are blend moisture content, length-to-diameter (L/D) ratio in the pellet die, and the blend ratio. A flat-die pellet mill was also used in this study. The pine and switchgrass blend ratios that were tested include (1) 25% 2-inch top pine residue with 75% switchgrass; (2) 50% 2-inch top pine residue with 50% switchgrass; and (3) 75% 2-inch top pine residue with 25% switchgrass. The pelleting process conditions tested included the L/D ratio in the pellet die (i.e., 1.5 to 2.6) and the blend moisture content (20 to 30%, w.b.). Analysis of experimental data indicated that blending 25% switchgrass with 75% 2-inch top pine residue and 50% switchgrass with 50% 2-inch top pine residue resulted in pellets with a bulk density of >550 kg/m3 and durability of >95. Optimization of the response surface models developed for process conditions in terms of product properties indicated that a higher L/D ratio of 2.6 and a lower blend-moisture content of 20% (w.b.) maximized bulk density and durability. Higher pine in the blends improved the pellet durability and reduced energy consumption. Tests were also conducted on blends of pine and switchgrass using a pilot scale pellet mill by varying the blend moisture content and compression ratio of the pellet die. Pelleting tests on pure pine and switchgrass and their blends in a pilot scale ring die pellet mill with a throughput of 1 ton/h has indicated pellets with durability >500 kg/m3 and durability in the range of >90 and >95 %. The demonstration of high moisture pelleting of blends of 2- and 6-inch top pine residues and switchgrass blends indicated that good quality pellets with durability >90 % and bulk density of >500 kg/m3 can be produced.

09 BIOMASS FUELS↗

DOE OSTI2021

Strongly Anisotropic Thermomechanical Response to Shock Wave Loading in Oriented Samples of the Triclinic Molecular Crystal 1,3,5-Triamino-2,4,6-trinitrobenzene

All-atom molecular dynamics (MD) simulations were used to study shock wave loading in oriented single crystals of the highly anisotropic triclinic molecular crystal 1,3,5-triamino-2,4,6-trinitrobenzene (TATB). The crystal structure consists of planar hydrogen-bonded sheets of individually planar TATB molecules that stack into graphitic-like layers. Shocks were studied for seven systematically prepared crystal orientations with limiting cases that correspond to shock propagation exactly perpendicular and exactly parallel to the graphitic-like layers. The simulations were performed for initially defect-free crystals using a reverse-ballistic configuration that generates explicit, supported shocks. Final longitudinal stress components are between ≈8.5 and ≈10.5 GPa for the 1.0 km s –1 impact speed studied. Orientation-dependent properties are reported including shock speeds, stresses, temperatures, compression ratios, and local material strain rates. Spatiotemporal maps of the temperature, stress tensor, material flow, and molecular orientations reveal complicated processes that arise for specific shock directions. Furthermore, the results indicate that TATB shock response is highly sensitive to crystal orientation, with significant qualitative differences for the time evolution of the stress tensor and temperature, elastic/inelastic compression response, defect formation and growth, critical von Mises stress, and strain rates during shock rise that span nearly an order of magnitude. A variety of inelastic deformation mechanisms are identified, ranging from crumpling of graphitic-like layers to dislocation-mediated plasticity to intense shear strain localization. To our knowledge, these are the first systematic MD simulations and analysis of explicit shock wave propagation along nontrivial crystal directions in a triclinic molecular crystal.

36 MATERIALS SCIENCE↗