Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Rapid compression machines”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Understanding the low-temperature chemistry of 1,2,4-trimethylbenzene

1,2,4-trimethylbenzene is an important representative aromatic component of gasoline/diesel/jet fuels and thus it is necessary to understand its low-temperature chemistry. In this paper, ignition delay times (IDTs) of both 1,2,4-trimethylbenzene (124TMB) and its blends with n -heptane were measured at engine-like conditions using both a high-pressure shock tube and a rapid compression machine for fuel in ‘air’ mixtures at pressures of 10 and 30 atm and at temperatures in the range 600 – 1100K. The experiments in this study show for the first time that 124TMB presents a two-stage ignition behavior at engine relevant conditions. Blending n -heptane with 124TMB can significantly increase mixture reactivity at temperatures below 1000K. A new detailed mechanism has been developed to simulate the experimentally measured IDT data. The mechanism can capture well the two-stage ignition behavior as well as the ignition delays at different pressures, equivalence ratios over a wide temperature range, for both pure fuels and their blended mixtures. Flux analyses show that the benzylic radicals (formed via H-atom abstraction from the methyl groups ortho-sites on 124TMB) can add to O 2 forming $\mathrm{R\dot{O}_2}$ radicals, which can isomerize to $\dot{Q}$OOH by intramolecular H-atom transfer from the ortho- methyl group and these $\dot{Q}$OOH radicals undergo a second addition to O 2 . This is analogous to the chain branching reaction pathways of alkanes. The chain branching reaction pathways are responsible for the first-stage heat release of 124TMB. In conclusion, the competitions between chain branching and both chain propagating and chain termination reaction pathways lead to a less pronounced negative temperature coefficient (NTC) behavior for 124TMB oxidation, compared to two-stage ignition behavior observed for alkanes and other fuels.

1,2,4-trimethylbenzene↗
DOE OSTI2021

Experimental and Kinetic Modeling Study of 3-Methyl-2-butenol (Prenol) Oxidation

Longer chain alcohols with 4–5 carbon atoms are attractive alternative fuels as they can be derived from biological sources and since their combustion leads to lower exhaust gas levels of NO x and soot compared to commercial fossil fuels. The auto-ignition behavior of fuels that contain both a hydroxyl group and a C=C double bond in their molecular structure is not well established in the literature. Understanding the influence of these functional groups on the ignition behavior of fuels is critical in the development of tailor-made fuels for advanced combustion engines. In this study, ignition delay times of an unsaturated alcohol, 3-methyl-2-butenol (prenol), are measured using a high-pressure shock tube and a rapid compression machine at pressures of 15 and 30 bar at equivalence ratios of 0.5, 1.0, and 2.0 in “air” in the temperature range 600–1400 K. Furthermore, a detailed kinetic model is developed and validated using the new experimental data in this study. In addition, speciation data in a jet-stirred reactor, ignition delay times, and laminar burning velocities available in the literature were also used to validate the new kinetic model. Fuel flux and sensitivity analyses are performed using this new model to determine the important fuel consumption pathways and critical reactions that affect the reactivity of prenol.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Methyl propyl ether combustion

Ethers are potential diesel blendstocks that have high cetane numbers and identified cost-effective production pathways. Among ethers, methyl propyl ether (MPE) is a small enough molecule to build a good detailed model for and a large enough molecule for studying key intramolecular hydrogen migrations involved in low-temperature feedback loops for larger ethers. In this study, a model was generated for the combustion of MPE from 600-900 K and 10-20 bar using the Reaction Mechanism Generator (RMG) package. Results from rapid compression machine (RCM) experiments were used to validate the kinetic model. At low temperatures, the generated model successfully captured the dominant low-temperature feedback loops. At higher temperatures, propanal is a major combustion intermediate for PME, suggesting that aldehydes likely play a significant role in larger ethers' combustion.

09 BIOMASS FUELS↗
DOE OSTI2020

Low Temperature Oxidation of Methylpropyl Ether

Ethers are potential high-performance fuels that can be produced from renewable carbon sources such as biomass, but their combustion properties are not clearly understood. The cetane numbers (CN) for ethers, a measure of autoignition tendency, can range from less than 10 to greater than 100, and predictive tools based upon the molecular structures are desirable in order to focus fuel development research. In this study, we used molecular modeling, chemical kinetic modeling, and experimental measurements to understand the important reactions that lead to the autoignition of methylpropyl ether (MPE), a model for ethers containing alkyl chains long enough for intramolecular hydrogen transfer. These reactions are known to be important in the autoignition of alkanes and ethers and we propose to investigate the relationship between alkyl and ether reaction pathways. A reaction mechanism for MPE was created using MIT's Reaction Mechanism Generator (RMG) and G4 quantum calculation results and models were tested using the ChemKin Pro suite of software. Results from atmospheric pressure, flow tube reactor experiments and rapid compression machine (RCM) experiments were used to test the kinetic model and suggest refinements.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Probing the antiknock effect of anisole through an ignition, speciation and modeling study of its blends with isooctane

In order to unravel the reaction pathways relevant to anisole co-oxidation within a fuel blend, a detailed study of isooctane/anisole blends was performed with the ULille RCM. Ignition delays as well as mole fraction profiles were measured during a two-stage ignition delay using sampling and GC techniques. These results are used to validate a kinetic model developed from ab initio calculations for the most relevant rate constants which included H-atom abstraction reactions from anisole, and reactions on the potential energy surfaces of methoxyphenyl + O 2 and anisyl + O 2 . Pressure dependent rate constants were computed for the methoxyphenyl + O 2 and anisyl + O 2 reactive systems using master equation code analysis. The new kinetic model shows good agreement with the experimental data. Dual brute-force sensitivity analysis was performed, on both first- and second-stages of ignition, allowing the identification of the most important reactions in the prediction of both ignition delays. It was observed that while pure anisole does not show NTC behavior, a 60/40 isooctane/anisole blend displays such behavior, as well as two-stage ignition. This suggests anisole addition may not be as beneficial to knock resistance as expected from its high octane number. The kinetic modeling results demonstrate the importance of H-abstraction reactions both from the methoxy group and from the aryl ring in ortho-position and the addition of the resultant radicals to O2, mostly leading to the formation of polar or non-aromatic products.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Pre-vaporized ignition behavior of ethyl- and propyl-terminated oxymethylene ethers

Oxymethylene ethers (OMEs) have been studied in recent years for use as compression ignition fuel blendstocks, but the methyl-terminated OMEs commonly studied exhibit properties that are poorly optimized for engine use and distribution. Recent work has shown that OMEs with larger (ethyl, propyl, or butyl) end groups may have superior properties for fuel usage/storage. In this work, we consider ignition of four OMEs - diethoxymethane (E-1-E), dipropoxymethane (P-1-P), ethoxy-(methoxy) 2 -ethane (E-2-E), and diisopropoxymethane (iP-1-iP) - as representatives of the possible effects of changes to OME structures. To our knowledge, ignition behaviors of the latter three fuels have not been studied prior to this work. Further, we find that all of the tested linear OMEs (E-1-E, P-1-P, and E-2-E) show two-stage ignition at low temperatures and nonlinear ignition behavior, consistent with literature on methyl-terminated OMEs and E-1-E. The nonlinear, branched OME (iP-1-iP) required higher pressure and temperature to ignite than the linear OMEs; further, this fuel experienced only single stage ignition and a linear ignition delay curve. By analogy to existing kinetic mechanisms for ethers and higher alcohols, the chemical basis for the observed trends are hypothesized. Faster ignition of E-2-E results from the additional oxymethylene group providing additional sites for ROO formation and more possible QOOH structures. Slower low temperature ignition of P-1-P is driven by lower H abstraction rates in comparison to E-1-E, however at high temperatures P-1-P ignites faster, driven by increasing abstraction from the additional H site on the propyl group that opens up additional QOOH formation pathways. iP-1-iP ignition is slowed significantly by preferential H abstraction from the central carbon of the isopropyl group, which is crowded and unlikely to bond with O 2 , however at high temperatures, abstraction from H sites on the methyl groups allows for the ROO cascade initiation and subsequent rapid ignition.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Plasma-Assisted Pre-Chamber Ignition System for Highly Dilute Stoichiometric Heavy-Duty Natural Gas Engines (Final Technical Report)

This project explored advanced ignition technologies to significantly enhance efficiency and reduce operating costs for heavy-duty natural gas engines operating at stoichiometric conditions, while meeting ultra-low NOx emission standards. The main goal was to develop and validate a plasma-assisted pre-chamber ignition system that could deliver at least a 2% increase in brake thermal efficiency (BTE) and a 4% decrease in total cost of ownership (TCO) compared to a typical multi-cylinder engine with three-way catalyst aftertreatment, ensuring compatibility with the expected 2027 EPA/CARB regulations. In the first half of the project, the research team concentrated on developing and testing plasma-assisted pre-chamber ignition using nanosecond pulsed discharges. Extensive experiments were conducted in an optically accessible rapid-compression and expansion machine, a constant-volume chamber, and an optical single-cylinder engine. Experiments were coupled with CFD simulations. The work produced unique insights into pre-chamber flame formation, jet ignition, dilution effects, and flame quenching at pressures, temperatures, and dilution levels relevant to engines. Although plasma-assisted ignition showed promise in controlled lab settings, the research also identified fundamental and practical challenges when applying this technology to real engine conditions. Midway through the project, a crucial pivot was made, guided by three key findings. First, the power electronics required for nanosecond plasma discharges were found to be too costly for commercial use, undermining the project’s cost-of-ownership goals. Second, nanosecond plasma ignition was highly sensitive to turbulent flow in the pre-chamber, resulting in lower ignition reliability than traditional spark under engine-like conditions. Third, achieving a truly diffuse low-temperature plasma at high pressures near top dead center was not possible, reducing the anticipated chemical enhancement benefits. These results collectively suggested that continuing with plasma-assisted ignition was unlikely to meet both efficiency and cost objectives. In response, the project shifted focus to a more realistic approach: enhancing traditional spark-based pre-chamber ignition with significantly less spark energy. Using insights gained earlier in the project, the team redesigned the pre-chamber to maintain high dilution tolerance and quick combustion, even with lower ignition energy. Testing confirmed that with optimized pre-chamber design and combustion timing, a lower-energy spark could reliably ignite highly diluted stoichiometric mixtures, reduce burn time, and boost thermal efficiency. Final engine testing and techno-economic analysis verified that this revised approach successfully achieved the project goals. The optimized pre-chamber ignition system provided over a 2% increase in calculated brake thermal efficiency compared to the baseline engine. Notably, the lower ignition energy and simplified hardware reduced component stress, extended maintenance intervals, and lowered the total cost of ownership. When used with stoichiometric operation and traditional three-way aftertreatment, the system remained compatible with near-zero NOx emissions targets without increasing cost or complexity in the emissions control system. In summary, although the project deviated from its initial plasma-assisted ignition idea, the work produced a more practical and commercially viable solution. The results show that precisely optimized, low-energy pre-chamber spark ignition can significantly improve efficiency and reduce overall ownership costs for heavy-duty natural gas engines. This directly aligns with DOE goals for cleaner, more efficient, and cost-effective transportation technologies.

03 NATURAL GAS↗
DOE OSTI2023

Constraining the Multiscale Structure of Geophysical Fields in Machine Learning: The Case of Precipitation

The use of deep-learning algorithms for estimating the value of geophysical variables from remotely sensed information is rapidly expanding. The typical objective function minimized in such algorithms is the mean square error (MSE), which is known to lead to smooth estimates with compressed dynamical range as compared to the true distribution of the variable of interest. Here, we introduce and evaluate alternative objective functions, focusing on the retrieval of precipitation rates from satellite passive microwave radiometric measurements using a deep convolutional neural network. For this testbed application, the results show that explicitly imposing the preservation of the statistical distribution and spatial wavelet power spectrum of the target variable allows to accurately reproduce extreme values and sharp gradients across multiple scales in this study.

58 GEOSCIENCES↗
DOE OSTI2023

Exploitation of Defects in High Entropy Ceramic Barrier Materials

A critical mission need exists to develop new materials that can withstand extreme environments and multiple sequential threats. High entropy materials, those containing 5 or more metals, exhibit many exciting properties which would potentially be useful in such situations. However, a particularly hard challenge in developing new high entropy materials is determining a priori which compositions will form the desired single phase material. The project outlined here combined several modeling and experimental techniques to explore several structure-property-relationships of high entropy ceramics in an effort to better understand the connection between their compositional components, their observed properties, and stability. We have developed novel machine learning algorithms which rapidly predict stable high entropy ceramic compositions, identified the stability interplay between configurational entropy and cation defects, and tested the mechanical stability of high entropy oxides using the unique capabilities at the Dynamic Compression Sector facility and the Saturn accelerator.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Atomic cluster expansion potential for large scale simulations of hydrocarbons under shock compression

We present an Atomic Cluster Expansion (ACE) machine learned potential developed for high-fidelity atomistic simulations of hydrocarbons, targeting pressures and temperatures near and above supercritical fluid regimes for molecular fluids. A diverse set of stoichiometries were covered in training, including 1:0 (pure carbon), 1:4 (methane), and 1:1 (benzene), and rich bonding environments sampled at supercritical temperatures, hydrogen rich, reactive mixtures where metastable stoichiometries arise, including 1:2 (ethylene) and 1:3 (ethane). A high-fidelity training database was constructed by performing large-scale quantum molecular dynamic simulations [density functional theory (DFT) MD] of diamond, graphite, methane, and benzene. A novel approach to selecting structures from DFT MD is also presented, which allows for the rapid selection of unique DFT MD frames from complex trajectories. Comparisons to DFT and experimental data demonstrate that the presented ACE potential accurately reproduces isotherms, carbon melting curves, radial distribution functions, and shock Hugoniots for carbon and hydrocarbon systems for pressures up to 100 GPa and temperatures up to 6000 K for hydrocarbon systems and up to 9000 K for pure carbon systems. This work delivers a potential that can be used for accurate, large-scale simulations of shocked hydrocarbons and demonstrates a methodology for fitting and validating machine learning interatomic potentials to complex molecular environments, which can be applied to energetic materials in future works.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Efficient Clustering of Software Vulnerabilities using Self Organizing Map (SOM)

The common vulnerabilities and exposures (CVE) database was created with a mission to ``identify, define, and catalog publicly disclosed cybersecurity vulnerabilities''. This rich body of information can be used to enable rapid and efficient response to secure and defend cyber operations and protect critical cyber infrastructure. The main goal of this paper is to develop a visual analytics tool to enable deep analysis of CVEs using unsupervised clustering techniques. We enhance our analysis by first mapping CVEs to hierarchical-classes in Common Weakness Enumeration (CWE) using information in the National Vulnerability Database (NVD). Both the mapping and the numerical representation of CVEs are enabled by V2W-BERT, which uses natural language processing of the extensive information in NVD to generate a large tabular database of 137,226 CVE entries from 1999 to 2020, where each CVE is represented by a vector of 768 numerical features. The vectorized data is processed by Self-Organizing Maps (SOM), which is an unsupervised machine learning technique for dimensionality reduction, visual representation and clustering. Using a Torus map of 6417 units, we achieve ~10-fold data compression of ~140k CVEs using SOM. The trained map is further clustered using standard K-means clustering into 138 clusters of CVEs. We conducted a brief investigation of the rich mapping of CVEs to best-matching-units to K-means clusters, as well as CVEs to CWEs. For example, this novel mapping provided insight into the role of CWE-59 and CWE-264 in several CVEs that is otherwise hard to explore in the original data. We conclude that our this novel approach will not only enable deep analysis of the complex relationships between CVEs and CWEs, but also a mechanism to quickly respond to and design mitigation actions for rapidly evolving vulnerabilities that have not been mapped to existing CWEs.

Panchal, Khyati↗
DOE OSTI2022

Deep learning for electron and scanning probe microscopy: From materials design to atomic fabrication

Machine learning and artificial intelligence (ML/AI) are rapidly becoming an indispensable part of physics research, with applications ranging from theory and materials prediction to high-throughput data analysis. In parallel, the recent successes in applying ML/AI methods for autonomous systems from robotics through self-driving cars to organic and inorganic synthesis are generating enthusiasm for the potential of these techniques to enable automated and autonomous experiment in imaging. Here, we discuss recent progress in application of machine learning methods in scanning transmission electron microscopy and scanning probe microscopy, from applications such as data compression and exploratory data analysis to physics learning to atomic fabrication.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Initial Testing of an In Situ Load Retention Aging Vessel

A thermal aging vessel instrumented with load cells was fabricated. The primary function of the vessel is to continuously monitor the in situ load retention of up to three compressed polymer coupons undergoing thermally accelerated aging under nitrogen. A secondary function is to enable gas sampling of the vessel headspace during thermal aging. Heating of the vessel is achieved using a custom heater jacket. To improve upon our conventional aging study methods which require periodic interruption of aging to perform load testing in an Instron machine at room temperature, this technology aims to automate/facilitate data acquisition/analysis, improve data quality, and enable uninterrupted compression of the polymer which represents the service condition. As an example case to assess functionality of the in situ vessel, the load retention of a siloxane elastomer material additively manufactured by direct-ink-writing (DIW) was measured at three different isothermal aging temperatures for ~1 month. Initial compression of the coupons while near the aging temperature was achieved by temporarily opening the heated vessel to access the interior chamber and manually tightening four nuts to drive the heated compression plate down onto the heated coupons. Initial testing demonstrated achievement of the primary load retention monitoring function. Unfortunately, the vessel leaked which prevented gas sampling; an active purge was used to maintain a nitrogen atmosphere. Welded or otherwise sealed joints, which could be implemented in a future design, would likely eliminate leak paths. To apply time-temperature superposition (TTS), a technique used to provide long-term prediction of the load retention from short-term isothermal data, the load retention needed to be calculated relative to the load at an estimated “equilibrium” time, after most of the transient viscoelastic physical relaxation occurred. The peak load immediately after compression could not be used as the load retention basis for two reasons: (1) age-related changes must be isolated from non-age-related physical relaxation before applying TTS and (2) the manual mechanism used to compress the specimens at the aging temperature was neither smooth nor repeatable which affected the peak load value. To better understand the effect of the mode of initial compression on the measured load, and possibly better estimate “equilibrium” physical relaxation times, systematic stress relaxation experiments were performed using an Instron machine with a thermal chamber. At a given temperature, the DIW polymer was compressed to a fixed strain in either a stepped or continuous manner at two different rates, then held at that strain for 24 hrs. The results indicated that, at a given temperature, the different stress relaxation curves appeared to converge to the same curve at some “equilibrium” time when the non-age-related physical relaxation was mostly complete. Though this observation suggests that the discontinuous manual compression employed by the vessel is feasible, a compression mechanism that is rapid, smooth, and repeatable would enhance its use.

36 MATERIALS SCIENCE↗
DOE OSTI2021

A novel machine learning-based optimization algorithm (ActivO) for accelerating simulation-driven engine design

A novel design optimization approach (ActivO) that employs an ensemble of machine learning algorithms is presented. The proposed approach is a surrogate-based scheme, where the predictions of a weak leaner and a strong learner are utilized within an active learning loop. The weak learner is used to identify promising regions within the design space to explore, while the strong learner is used to determine the exact location of the optimum within promising regions. For each design iteration, exploration is done by randomly selecting evaluation points within regions where the weak learner-predicted fitness is high. The global optimum obtained by using the strong learner as a surrogate is also evaluated to enable rapid convergence once the most promising region has been identified. First, the performance of ActivO was compared against five other optimizers on a cosine mixture function with 25 local optima and one global optimum. In the second problem, the objective was to minimize indicated specific fuel consumption of a compression-ignition internal combustion (IC) engine while adhering to desired constraints associated with in-cylinder pressure and emissions. In this work, the efficacy of the proposed approach is compared to that of a genetic algorithm, which is widely used within the internal combustion engine community for engine optimization, showing that ActivO reduces the number of function evaluations needed to reach the global optimum, and thereby time-to-design by 80%. Furthermore, the optimization of engine design parameters leads to savings of around 1.9% in energy consumption, while maintaining operability and acceptable pollutant emissions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Significantly Promoting the Thermal Conductivity and Machinability of Negative Thermal Expansion Alloy via In Situ Precipitation of Copper Networks

Rapid advancements in electronic devices yield an urgent demand for high-performance electronic packaging materials with high thermal conductivity, low thermal expansion, and great mechanical properties. However, it is a great challenge for current design philosophies to fulfill all the requirements simultaneously. Here, an effective strategy is proposed for significantly promoting the thermal conductivity and machinability of negative thermal expansion alloy (Zr,Nb)Fe 2 through eutectic precipitation of copper networks. The eutectic dual-phase alloy exhibits an isotropic chips-matched thermal expansion coefficient and a thermal conductivity enhancement exceeding 200% compared with (Zr,Nb)Fe 2 , along with an ultimate compressive strength of 550 MPa. The addition of copper reorganizes the composition of (Zr,Nb)Fe 2 , which smooths the magnetic transition and shifts it toward higher temperature, resulting in linear low thermal expansion in a wide temperature range. The highly fine eutectic copper lamellae construct high thermal conductivity networks within (Zr,Nb)Fe 2 , serving as highways for heat transfer electrons and phonons. The in situ forming of eutectic copper lamellae also facilitates the mechanical properties by enhancing interfacial bonding and bearing additional stress after yielding of (Zr,Nb)Fe 2 . This work provides a novel strategy for promoting thermal conductivity and mechanical properties of negative thermal expansion alloys via eutectic precipitation of copper networks.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Recent Advances toward Efficient Calculation of Higher Nuclear Derivatives in Quantum Chemistry

In this article, we provide an overview of state-of-the-art techniques that are being developed for efficient calculation of second and higher nuclear derivatives of quantum mechanical (QM) energy. Calculations of nuclear Hessians and anharmonic terms incur high costs and memory and scale poorly with system size. Three emerging classes of methods—machine learning (ML), automatic differentiation (AD), and matrix completion (MC)—have demonstrated promise in overcoming these challenges. We illustrate studies that employ unsupervised ML methods to reduce the need for multiple Hessian calculations in dynamics simulations and those that utilize supervised ML to construct approximate potential energy surfaces and estimate Hessians and anharmonic terms at reduced cost. By extension, if electronic structure operations could be written in a manner similar to functions underlying ML methods, rapid differentiation or AD routines can be employed to inexpensively calculate higher arbitrary-order derivatives. While ML approaches are typically black-box, we describe methods such as compressed sensing (CS) and MC, which explicitly leverage problem-specific mathematical properties of higher derivatives such as sparsity and low-rank, to complete higher derivative information using only a small, incomplete sample. The three classes of methods facilitate reliable predictions of observables ranging from infrared spectra to thermal conductivity and constitute a promising way forward in accurately capturing otherwise intractable higher-order responses of QM energy to nuclear perturbations.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2026

Enabling accurate chemical modeling of shocked energetic materials using a machine learning interatomic potential

Understanding the complex chemistry of organic materials under dynamic compression is important for many applications, but it is challenging due to the large number of reactions occurring at various time scales. Here, in this study, we develop a machine learning potential based on Chebyshev polynomials to study the insensitive energetic material 1,3,5-triamino-2,4,6-trinitrobenzene (TATB) under detonation. We discuss a strategy for constructing diverse training data needed to capture the complex chemistry of TATB. Our potential demonstrates strong transferability across a wide range of thermodynamic conditions and other explosives, enabling accurate and reliable chemical modeling of organic materials under extreme conditions. The efficiency of our approach allows for simulations over several nanoseconds and for large system sizes, providing detailed insights into the chemistry of shocked TATB. The model accurately reproduces experimental Hugoniot equation of state data, and our simulations reveal the rapid formation of nitrogen-rich carbon clusters following shock. The methods and datasets developed here offer a robust framework for accurate chemical modeling of other shocked organic energetic materials.

Chemistry↗