Search NASA⌕ Search

SEARCH · Search NASA

Results for “Models, Chemical”

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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Photochemically Induced Acousto-optics Fluid Simulations

PIAFS is a finite-difference code to solve the compressible Navier-Stokes equations with chemical heating on Cartesian grids. It models chemical reactions of air (oxygen and carbon dioxide) with ozone subject to radiation. It uses a high-order WENO spatial discretization and explicit Runge-Kutta time integration. It is capable of parallel simulations using MPI. The code is written in C/C++.

Oudin, AlbertineN [Lawrence Livermore National Lab↗
DOE OSTI2026

Modeling Radiolysis and Chemical Reactions during Dry Storage of Aluminum-clad Spent Nuclear Fuel

After aluminum-clad spent nuclear fuel (ASNF) is removed from the reactor, it is initially stored in spent fuel pools, which are specially designed water-filled basins that provide temporary cooling to reduce the temperature of the fuel assemblies and provide radiation shielding. ASNF continues to generate heat due to the radioactive decay of elements within the fuel, which persists for many years post-shutdown as the residual radioactive products decay into more stable elements. During the wet storage period, an oxyhydroxide layer composed of boehmite/bayerite forms on the surfaces of the aluminum cladding from exposure to water in the pools. Road-ready packaging for long-term disposition of the ASNF involves dry storage in helium backfilled DOE standard canisters (DSCs). When the ASNF is removed from water storage and dried, most of the water is removed, but some physisorbed and chemisorbed water remains in the oxyhydroxide layers. This residual water can produce hydrogen when exposed to radiation from the ASNF during dry storage. Predicting hydrogen accumulation over time in the DSCs is critical for long-term storage considerations. Previous modeling efforts have developed coupled computational fluid dynamics (CFD)-chemical models to simulate temperature, pressure, and gas phase concentrations within the DSCs. These models use the thermal field predicted by CFD as input to a radiolysis model for the gas phase and the surface oxyhydroxide layer chemistry. Given the long storage period of the DSCs and the impracticality of long-term experiments, a simulation-based approach is necessary to assess chemical evolution within the canisters. This study advances the development of a modeling framework designed to simulate the chemical evolution of spent fuel canisters. Both thermal and radiation-driven reactions are considered, with radiation kinetics quantified using G-values. Sensitivity analysis identifies key parameters influencing species composition. Reaction pathway diagrams offer insight into dominant species formation routes, enabling more effective comparisons between model predictions and experimental observations, particularly regarding the production of hydrogen. Results show that the model predicts significant hydrogen gas production with minimal oxygen generation, primarily due to hydrogen formation via boehmite pathways. These findings underscore the importance of accurately characterizing surface-bound species and radiolysis kinetics. A deeper understanding of these mechanisms is critical for evaluating the long-term safety of nuclear waste storage.

12 - MGMT OF RADIOACTIVE AND NON-RADIOACTIVE WASTE↗
DOE OSTI2025

Enhancing Drinking Water Quality Modeling: Leveraging Physics Informed Neural Networks for Learning with Imperfect Reaction Models and Partial Data

Chemical kinetics models, typically formulated as systems of ordinary or partial differential equations, are valuable tools for simulating drinking water quality. However, these models often face inaccuracies due to discrepancies between the laboratory and the real-world conditions, as well as limitations in experimental analytical methods, hindering the accurate representation of the true underlying chemical mechanisms. In this study, we propose a Physics Informed Neural Network (PINN), using the eXtreme Theory of Functional Connections, to improve the prediction of chemical concentrations over time. The PINN method accounts for imperfect chemical models and incorporates partial data to improve predictions. Focusing on reactions describing water disinfection residual and disinfectant byproduct formation, which are crucial for public health and regulatory compliance, we demonstrate that the PINN model is able to accurately predict the concentrations of chemical species across various pH values. Notably, the model extends its accuracy to predict concentrations of chemical species not originally included in its training data. The developed method can be extended to a variety of chemical systems, offering a wide array of potential applications.

13 HYDRO ENERGY↗
DOE OSTI2024

Reactive extrusion of frontally polymerizing continuous carbon fiber reinforced polymer composites

The manufacturing of carbon fiber-reinforced polymer (CFRP) composites demands rapid and energy-efficient strategies. Frontal polymerization (FP) enables the manufacturing of CFRP using dicyclopentadiene (DCPD) thermoset polymer which meets these requirements. In this work, we introduce reactive extrusion of CFRP (RE-CFRP), where two rollers provide localized heat and pressure to sustain the curing reaction and the consolidation of a continuous carbon fiber tow pre-impregnated with DCPD. We study the effect of the extrusion speed, temperature, and compaction force on the properties of the produced CFRP. Mechanical testing confirms that the resulting fiber volume fraction and the elastic modulus are similar to bulk cured tows. A homogenized thermo-chemical model is developed to study the effect of the process parameters on the polymerization reaction. The process produces hollow woven composite tubes directly via extrusion and in situ curing. Overall, this process offers advantages in curing, tooling, speed, and energy.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Estimating ionization states and continuum lowering from ab initio path integral Monte Carlo simulations for warm dense hydrogen

Warm dense matter (WDM) is an active field of research, with applications ranging from astrophysics to inertial confinement fusion. Ionization degree and continuum lowering are important quantities to understand how materials behave under these conditions, but can be difficult to diagnose since experimental campaigns are limited and often require model-dependent analysis. This is especially true for hydrogen, which has a comparably low scattering cross section, making high-quality data particularly difficult to obtain. Consequently, building equation of state tables often relies on simulations in combination with untested approximations to extract properties from experiments. Here, we investigate an approach for extracting the ionization potential depression and ionization degree—quantities which are otherwise not directly accessible from the physical model—from first-principles path integral Monte Carlo (PIMC) simulations utilizing a chemical model. In contrast to experimental measurements, where noise and nonequilibrium effects add to the uncertainty of the inferred parameters, PIMC simulations provide a clean signal with well-defined thermodynamic conditions. Comparisons against commonly used models show a qualitative agreement, but we find deviations primarily for the high-density and high-temperature cases. We also demonstrate the decreasing sensitivity of the dynamic structure factor with respect to both ionization and continuum lowering for increasing scattering angles in x-ray Thomson scattering experiments. Our work has important implications for the design of future experiments, but also offers qualitative understanding of structure factors and the imaginary-time correlation function obtained from first-principles quantum Monte Carlo simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Foundation models for atomistic simulation of chemistry and materials

Conventional computational methods for modeling chemical and materials systems are limited by system size and timescale, forcing a trade-off between quantum-mechanical accuracy and the sampling needed for realistic observables. Large language and vision foundation models — pre-trained on massive datasets using transformer architectures — have revolutionized many fields. It is thus interesting to ask whether a foundation model — subject to suitable data, parameter scaling and training — could enable learned simulations of chemistry and materials. Here, in this study, we review the field of machine-learned interatomic potentials (MLIPs) and posit that scaling up large and diverse chemical and materials datasets and highly expressive architectures using advanced training strategies should result in models that are: more efficient, transferable, robust to out-of-distribution scenarios, and easier to fine-tune to a variety of downstream physical observables than models trained from scratch on small datasets corresponding to specific, targeted atomistic simulation tasks. We provide specific criteria for creating such large-scale MLIP foundation models, coordinated strategies for their development, evaluation and deployment, and highlight potential emergent capabilities that could transform predictive simulations in chemistry and materials science and accelerate discovery across multiple technological domains.

Yuan, Eric C.-Y. [University of California, Berkel↗
DOE OSTI2025

High Pressure Measurement of Soot Formation Applicable to Energetic Materials Fireballs

The addition of hydrogen and oxygen to a hydrocarbon fuel mixture has a significant effect on its sooting tendencies at high pressures. Understanding the mechanism behind the formation of soot is key to proper chemical modeling of fireballs. The objective of this research is to investigate formation rates and soot induction times of intermediary chemicals resulting from energetic material combustion. Three intermediaries, acetylene (C2H2), ethylene (C2H4), and propyne (C3H4-P) are studied. Here, a laser spectroscopy system was utilized to measure soot formation, induction time, and detail the time histories of soot experiments performed using the University of Central Florida high pressure shock tube facility.

Loye, Timothy [University of Central Florida, Orla↗
DOE OSTI2026

Optimization of Desalination Systems with Detailed Water Chemistry through Integration of Reaktoro in WaterTAP

Chemistry predictions are critical for an accurate estimation of performance and costs in desalination process models, which allows for the estimation of the value of new technologies and the viability of treating new water sources. Herein, we present how an implicit function formulation can be used to integrate the chemical modeling package, Reaktoro, into the techno-economic assessment and modeling platform, WaterTAP. This approach resolves the critical issues of integrating large-scale thermodynamic models and databases into equation-oriented process models while allowing more flexibility relative to previously presented surrogate-based methods. We describe how this integration into Pyomo and WaterTAP models is implemented and used through the open-source package Reaktoro-PSE . We first validate this integration approach by performing optimization on a previously presented desalination treatment train with softening and acid addition as the pretreatment steps. Then, to demonstrate the value of this approach, we extend the cost-optimization problem to include the simultaneous addition of lime and soda ash for softening, and HCl and H 2 SO 4 in the acidification steps. Finally, we were able to confirm the previously established results that were obtained by using surrogate models and demonstrate that the implicit function approach enables exploration of different feedwater compositions and a larger number of chemicals and their combinations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Disentangling Acid‐Base Chemistry through Blue Shifting Hydrogen Bond Contributions

The blue shifting of vibrational frequencies in hydrogen bonded molecules, as observed in aqueous environments, has been attributed to local partial charge transfer from solvation. Here, in this study, we extrapolate the blue shift model to the stronger ionic interactions between hydrogen bond acceptors associated with protonation through augmented pH levels and competitive interactions with counter ion pairing. The chemical model we utilize in this work is the aqueous pyridine‐pyridinium equilibrium to characterize the blue shifts observed in the pyridinium chloride ionic system. The observed agreement between observed experimental and calculated spectral shifts shows that the blue shifting model can be extrapolated to stronger interactions and accurately describe the nature of the hydrogen bond.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Gas-Phase Oxidation Mechanisms of Nitrogen-Containing Organic Compounds

Nitrogen-containing organic molecules play essential roles in nearly every aspect of chemistry. The importance of these species within gas-phase chemistry has increased in recent years, due to their emissions from wildfires, crude biofuels, ammonia combustion, and CO 2 capture facilities. However, there is a lack of detailed relevant mechanistic and kinetic studies of N-compounds, resulting in poor representation of these pathways within chemical models. In this report, we detail our exploration of the gas-phase oxidation mechanisms of pyrrole, imidazole, pyrrolidine, methylamine, and dimethylamine, done by employing multiplexed VUV photoionization mass spectrometry coupled with KinBot, a computational tool that automatically explores multi-well potential energy surfaces. Overall, we vastly expanded the known gas-phase oxidation mechanisms and chemical kinetics of these species. This work will improve the accuracy and completeness of atmospheric and combustion chemistry models, ultimately leading to better-informed decision-makers in the energy and environmental sectors.

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

Personal and environmental predictors of polycyclic aromatic hydrocarbon exposure identified through repeated silicone wristband sampling

This study integrates quantitative data on personal exposure to polycyclic aromatic hydrocarbons (PAHs) in 162 silicone wristbands with demographics, behavioral information, and housing characteristics to explore contributions to residential exposure in a superfund-adjacent community over the course of a year. Forty-six residents completed questionnaires and wore silicone wristbands as personal passive samplers for seven consecutive days on up to four separate occasions in alternating months between November 2022 and June 2023. It was hypothesized that individual behaviors and housing characteristics are sources of dependence and correlation between personal PAH exposures. 50 PAHs were detected at least once, 17 of which were alkylated PAHs. Exposure to PAHs of similar molecular weight was often correlated, notably between naphthalenes (2-rings) and higher molecular weight PAHs (3 or more rings). Generalized linear mixed models identified flooring type, participant age, and sampling month as important predictors of increased PAH exposure, and flooring type, and use of wood stoves or heavy machinery as predictors of increased naphthalene exposure relative to higher molecular weight PAHs. Individual chemical models based on concentration data and detection frequencies corroborated these findings across multiple PAHs. We demonstrate that personal exposure is not static and the degree of variability in personal exposure is individual. Hence, identification of influential exposure factors through repeated measures of chemical exposure and characterization of variability in personal exposure as performed in this study, is important in the development of exposure mitigation strategies.

Bonner, Emily↗
DOE OSTI2025

Soot formation and precursor chemistry in Counterflow flames of aviation fuel surrogates

To meet market demands, the aviation sector is interested in utilizing drop-in Synthetic Aviation Turbine Fuels (SATF), either as neat fuels or in blends with conventional Jet A. SATF currently approved in standard specifications may have lower aromatic content with significant fractions of normal, branched, and cyclo-alkanes. Fundamental studies on soot formation from aviation fuels (Jet A, SATF) and their surrogate components are essential to understand how fuel composition influences soot and soot precursor formation. Here, this study reports new measurements of polycyclic aromatic hydrocarbons (PAH) and soot in counterflow diffusion flames (CDFs) of aviation fuel surrogates. Both intrusive and non-intrusive diagnostics are employed to determine the profiles of temperature, gas phase species, PAHs (up to C16), and soot volume fraction (SVF) in CDFs of iso-octane and surrogate mixtures. These measurements shed light on the transition of soot precursors to primary soot particles. In addition to serving as a common surrogate component in Jet A surrogate mixtures, iso-octane is a template species for larger, less volatile branched alkanes found in SATF mixtures. The newly developed Lawrence Livermore National Laboratory (LLNL) PAH and soot model successfully captures temperature, precursor species, and SVF profiles for the mixtures and conditions discussed in this work. Finally, a high-fidelity surrogate for Jet A is proposed that matches targeted physical and chemical properties well, while leveraging the wide range of candidate fuel molecules available in the LLNL detailed chemical model. The proposed surrogate formulation is validated against newly acquired measurements of the surrogate and literature measurements of Jet A. These new experiments and simulations provide critical insights into the PAH and soot formation from aviation fuels. Reaction pathways which require further investigation are highlighted, such that future work may bridge the remaining quantitative gaps in predicting soot formation from aviation fuel surrogates and surrogate components.

Aviation fuels↗
DOE OSTI2025

Chemical templates of the Central Molecular Zone

Context . The Central Molecular Zone (CMZ) of the Milky Way exhibits extreme conditions, including high gas densities, elevated temperatures, enhanced cosmic-ray ionization rates, and large-scale dynamics. This makes it a perfect laboratory for astrochemical studies. With large-scale molecular surveys revealing increasing chemical and physical complexity in the CMZ, it is essential to develop robust methods to decode the chemical information embedded in this extreme region. Aims . A key step to interpreting the molecular richness found in the CMZ is building chemical templates tailored to its diverse conditions. In particular, understanding how CMZ environments affect shock and protostellar chemistry is crucial. The combined impact of high ionization, elevated temperatures, and dense gas remains insufficiently explored for observable tracers. Methods . For this study, we utilized UCLCHEM , a gas-grain time-dependent chemical model, to link physical conditions with their corresponding molecular signatures and identify key tracers of temperature, density, ionization, and shock activity. To achieve this, we ran a grid of models of shocks and protostellar objects representative of typical CMZ conditions, focusing on 24 species, including complex organic molecules. Results . Shocked and protostellar environments show distinct evolutionary timescales (≲10 4 vs. ≳10 4 years); 300 K emerges as a key temperature threshold for chemical differentiation. We find that cosmic-ray ionization and temperature are the main drivers of chemical trends. HCO + , H 2 CO, and CH 3 SH trace ionization, while HCO, HCO + , CH 3 SH, CH 3 NCO, and HCOOCH 3 show consistent abundance contrasts between shocks and protostellar regions over similar temperature ranges. Conclusions . We characterized the behavior of 24 species in protostellar and shock-related environments. While our models underpredict some complex organics in shocks, they reproduce observed trends for most species, supporting scenarios involving a need for recurring shocks in Galactic Center clouds and enhanced ionization toward Sgr B2(N2). Future work should assess the role of shock recurrence and metallicity in shaping chemistry.

Galaxy: center↗
DOE OSTI2024

An integrated experimental–modeling approach to identify key processes for carbon mineralization in fractured mafic and ultramafic rocks

Abstract Controlling atmospheric warming requires immediate reduction of carbon dioxide (CO2) emissions, as well as the active removal and sequestration of CO2 from current point sources. One promising proposed strategy to reduce atmospheric CO2 levels is geologic carbon sequestration (GCS), where CO2 is injected into the subsurface and reacts with the formation to precipitate carbonate minerals. Rapid mineralization has recently been reported for field tests in mafic and ultramafic rocks. However, unlike saline aquifers and depleted oil and gas reservoirs historically considered for GCS, these formations can have extremely low porosities and permeabilities, limiting storage volumes and reactive mineral surfaces to the preexisting fracture network. As a result, coupling between geochemical interactions and the fracture network evolution is a critical component of long-term, sustainable carbon storage. In this paper, we summarize recent advances in integrating experimental and modeling approaches to determine the first-order processes for carbon mineralization in a fractured mafic/ultramafic rock system. We observe the critical role of fracture aperture, flow, and surface characteristics in controlling the quantity, identity, and morphology of secondary precipitates and present where the influence of these factors can be reflected in newly developed thermo-hydro-mechanical–chemical models. Our findings provide a roadmap for future work on carbon mineralization, as we present the most important system components and key challenges that we are overcoming to enable GCS in mafic and ultramafic rocks.

58 GEOSCIENCES↗
DOE OSTI2025

Model-free Rayleigh weight from x-ray Thomson scattering measurements

X-ray Thomson scattering (XRTS) has emerged as a powerful tool for the diagnostics of matter under extreme conditions. In principle, it gives one access to important system parameters such as the temperature, density, and ionization state, but the interpretation of the measured XRTS intensity usually relies on theoretical models and approximations. In this context, a key property is given by the Rayleigh weight that describes the electronic localization around the ions. Here, we show that it is possible to extract the Rayleigh weight directly from the experimental data without the need for any model calculations or simulations. As a practical application, we consider an experimental measurement of strongly compressed Be at the National Ignition Facility [Döppner et al., Nature 618, 270–275 (2023)]. We demonstrate that experimental results for the Rayleigh weight open up new avenues for the interpretation of XRTS experiments by matching the measurement with ab initio simulations such as density functional theory or path integral Monte Carlo. Interestingly, this new procedure leads to significantly lower density compared to previously used chemical models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Unraveling electronic correlations in warm dense quantum plasmas

The study of matter at extreme densities and temperatures has emerged as a highly active frontier at the interface of plasma physics, material science and quantum chemistry with relevance for planetary modeling and inertial confinement fusion. A particular feature of such warm dense matter is the complex interplay of Coulomb interactions, quantum effects, and thermal excitations, making its rigorous theoretical description challenging. Here, we demonstrate how ab initio path integral Monte Carlo simulations allow us to unravel this intricate interplay for the example of strongly compressed beryllium, focusing on two X-ray Thomson scattering data sets obtained at the National Ignition Facility. We find excellent agreement between simulation and experiment with a very high level of consistency between independent observations without the need for any empirical input parameters. Our results call into question previously used chemical models, with important implications for the interpretation of scattering experiments and radiation hydrodynamics simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Multiphysics Time-Integration for Turbulent Combustion at the Exascale

Turbulent reacting flow systems are often modeled with coupled time-dependent partial differential equations (PDEs). Solving such equations can easily tax the world's largest supercomputers. One pragmatic strategy for attacking such problems is to split the PDEs into components that can more easily be solved in isolation. This generic operator-splitting strategy leads to a set of ordinary differential equations (ODEs) that need to be solved as part of an "outer-loop" time-stepping approach. In many combustion applications, the ODEs to be solved can be very stiff, exhibiting timescales that span many orders of magnitude. The SUNDIALS library provides a plethora of robust time integration algorithms for solving these ODEs on exascale-capable computing hardware, yet for many complex applications (such multicomponent fuels or emissions predictions), the chemical models remain too complex to solve using reasonable resources. The Quasi-Steady State Approximation (QSSA) can be an effective tool for reducing the size and stiffness of the simulations. In this talk, I will discuss the use of the SUDIALS library of ODE solvers together with automatic code generation tools to solve complex turbulent reacting flow problems using QSSA models.

chemistry↗
DOE OSTI2025

The composition of gases from a diffusion flame above longleaf pine needle fuel beds

The gas and tar composition of a diffusion flame from longleaf pine needles is currently poorly understood and more data are needed to fill in the gap between pyrolysis data and smoke plume data, thus improving physical and chemical modeling of wildland smoke formation. A pilot experiment to measure light gas and tar composition of such a flame is described for three flame regions: persistent flame (flame base), intermittent flame, and smoke plume. Flame gases from 24 experimental fires were collected in canisters and analyzed using EPA method TO-14A for CO 2 , CO, H 2 , CH 4 , and C 2 to C 7 hydrocarbon gases. Condensed gas (tar) samples were collected and analyzed using GC/MS. Other light gases were measured using FTIR spectroscopy. Results from compositional data analysis suggest significant differences in (relative) concentration of compounds detected in the three regions of the flame. Statistical tests for differences in flame zones were performed using the canister data: Concentration of hydrocarbons relative to CO and CO 2 decreased from the persistent flame zone above the pyrolyzing needles through the intermittent flame region into the flame-free plume. This was likely due to both chemical reactions (oxidation) occurring in the flame as well as the introduction of air into the flame/plume by entrainment.

Biomass↗