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At least 1,387 records · Page 77

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DOE OSTI2024

A framework for strategic discovery of credible neural network surrogate models under uncertainty

The widespread integration of deep neural networks in developing data-driven surrogate models for high-fidelity simulations of complex physical systems highlights the critical necessity for robust uncertainty quantification techniques and credibility assessment methodologies, ensuring the reliable deployment of surrogate models in consequential decision-making. Here, this study presents the Occam Plausibility Algorithm for surrogate models (OPAL-surrogate), providing a systematic framework to uncover predictive neural network-based surrogate models within the large space of potential models, including various neural network classes and choices of architecture and hyperparameters. The framework is grounded in hierarchical Bayesian inferences and employs model validation tests to evaluate the credibility and prediction reliability of the surrogate models under uncertainty. Leveraging these principles, OPAL-surrogate introduces a systematic and efficient strategy for balancing the trade-off between model complexity, accuracy, and prediction uncertainty. The effectiveness of OPAL-surrogate is demonstrated through two modeling problems, including the deformation of porous materials for building insulation and turbulent combustion flow for ablation of solid fuels within hybrid rocket motors.

42 ENGINEERING↗

DOE OSTI2025

A discontinuous Galerkin spectral element method for compressible reacting flows

High-order methods have recently been shown to be an effective tool for high-fidelity flow computations like direct numerical simulations and large-eddy simulations because of their strong balance between accuracy and computational cost. In this work, a high-order discontinuous Galerkin spectral element method (DGSEM) is developed to solve the chemically reacting Navier-Stokes equations. To handle the disparate length and time scales associated with these equations, we develop a novel method which combines the spectral accuracy of the SEM with the flexibility of the DG approach. The framework, implemented in the spectral element code Nek5000, is well suited to capture turbulence in smooth regions of the flow, while maintaining numerical stability in the presence of shocks. An entropy-residual based artificial viscosity is added to smooth shocked regions of flow, and a positivity-preserving limiter is implemented to suppress non-physical oscillations. These enhancements support the numerical stability of the hydrodynamic sub-step, which is decoupled from the chemistry integration through a second-order operator splitting method. Here, a series of smooth and discontinuous validation cases are presented in increasing physical and computational complexity for both inviscid and viscous flows. In particular, simulations of canonical one-dimensional and two-dimensional detonations are performed, and the high-order numerical results are validated against available literature data. Additional validation studies are carried out for classical three-dimensional numerical simulations of incompressible and compressible turbulent flows.

Compressible reacting flows↗

DOE OSTI2025

Generalizable machine learning potentials for quantum-accurate predictions of non-equilibrium behavior in 2D materials

Machine learning interatomic potentials (ML-IAPs) are emerging as transformative tools in materials modeling, promising quantum-level accuracy at a fraction of the computational cost. However, their ability to generalize beyond equilibrium configurations and to reliably capture defect- and temperature-driven behavior remains underexplored. Here, we develop and benchmark two state-of-the-art ML-IAPs, Spectral Neighbor Analysis Potential (SNAP) and Allegro, on a comprehensive dataset for monolayer MoSe₂. Using density functional theory (DFT) as the reference, we evaluate their performance in capturing stress–strain behavior, phase transition energetics, defect evolution, edge stability, and fracture toughness. Allegro, a deep equivariant neural network potential, surpasses both SNAP and the classical Tersoff potential in accuracy, efficiency, and transferability. Importantly, both ML potentials accurately reproduce experimental fracture measurements and ab initio predictions of inversion domain formation—phenomena well beyond their training sets. Our findings establish ML-IAPs as viable replacements for traditional force fields in the study of non-equilibrium mechanical phenomena, enabling large-scale, high-fidelity simulations in 2D materials and beyond. In conclusion, this work provides a broadly applicable framework for data-driven modeling of structural and functional transformations under extreme conditions.

2D materials↗

DOE OSTI2025

A Computational Framework to design 3D stiffness gradient acoustic metamaterials for impedance matching

Acoustic waves play a crucial role in various applications, including medical imaging, non-destructive testing, and sonar systems. One of the significant challenges in these applications is impedance matching, which is essential for minimizing reflections and maximizing the transfer of acoustic energy between different media. Acoustic metamaterials offer a promising solution to this challenge. In addition to impedance control, gradient stiffness can enhance structural efficiency and enable spatial control of wave propagation, making it a valuable feature in acoustic metamaterial design. In this pa- per, we present our developed computational method to design 3D stiffness gradient acoustic metamaterials for impedance matching. The key steps in our approach include generating initial designs using a periodic covariance function to provide unit cells that are both periodic on the boundaries and randomly formed inside the unit cell. Furthermore, we integrated manufacturing constraints into the design process, ensuring that the structures are interconnected for fabrication. We propose two computational optimization algorithms: GenUnit, based on a non-dominated sorting genetic algorithm (NSGA-II), and MLMatch, which leverages differentiable machine learning. The two approaches are not separate contributions but complementary com- ponents of a unified framework. GenUnit requires no training data and directly interfaces with physics-based simulations, making it highly accurate but slower for large-scale exploration. In contrast, MLMatch is data-hungry during training but, once trained, enables near-instantaneous inference and broad design-space coverage. Together, they form a hybrid strategy: ML- Match rapidly explores the global design space, and GenUnit provides local refinement with high-fidelity accuracy. This balance between training cost, inference time, and precision is the motivation for including both methods in the same study. We applied this dual-algorithm framework to generate two metallic-based metamaterial designs that match the acoustic impedance of water while exhibiting a controlled gradient in stiffness (from stiff to soft). The stiffness gradient is particularly advantageous in applications where one side of the structure must interface with soft or sensitive surfaces, such as human tissue or delicate components. Here, this work paves the way for improved materials in various acoustic applications, particularly in ultrasound devices, by providing better impedance.

Metamaterial↗

DOE OSTI2021

Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery

Machine learning (ML)-accelerated discovery requires large amounts of high-fidelity data to reveal predictive structure–property relationships. For many properties of interest in materials discovery, the challenging nature and high cost of data generation has resulted in a data landscape that is both scarcely populated and of dubious quality. Data-driven techniques starting to overcome these limitations include the use of consensus across functionals in density functional theory, the development of new functionals or accelerated electronic structure theories, and the detection of where computationally demanding methods are most necessary. When properties cannot be reliably simulated, large experimental data sets can be used to train ML models. In the absence of manual curation, increasingly sophisticated natural language processing and automated image analysis are making it possible to learn structure–property relationships from the literature. Finally, models trained on these data sets will improve as they incorporate community feedback.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Temperature measurements in heavily-sooting ethylene/air flames using synchrotron x-ray fluorescence of krypton

High-fidelity temperature field measurements have been made for several heavily-sooting ethylene/air flames that have historically been challenging environments for conventional optical diagnostics. These challenges have largely been overcome here, in this study, by conducting x-ray fluorescence (XRF) measurements of a Kr fluorescent agent in the hard x-ray regime (15 keV). The current methodology presents a more economical diagnostic than a previously reported implementation of the Kr-XRF method, by limiting seeding of the expensive fluorescent agent to only the fuel stream flows. Detailed reacting flow simulations have been used to interpret experimental signals by tracking the mole fraction of the fluorescent agent in the flow field. Simulated Kr densities are in excellent agreement with measurements throughout the flow field. Temperature measurements of the flow field also agree well with simulations and recent literature studies. However, uncertainties in the measurements become increasingly large downstream of the burner surface as the krypton fraction drops due to mixing of the fuel and co-flow streams. Additionally, we demonstrate that soot particles in the heavily sooting flames studied do not impede the Kr-XRF measurements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Cost-constrained adaptive simulations of transient spray combustion in a gas turbine combustor

Predictive high-fidelity simulations of turbulent spray combustion must capture the combined effects of complex chemistry, multiphase evaporating flow and spray-flame interactions to achieve physical accuracy. Finite-rate chemistry (FRC) combined with a realistic chemical mechanism is a combustion model well-suited for this purpose, but has a high computational cost due to the large number and stiffness of transported chemical species. In contrast, flamelet-based models achieve lower cost by transporting a small number of quantities of reduced stiffness, but assumptions regarding local flame topology, boundary conditions and inter-phase coupling limit their physical accuracy. Recently, the Pareto-efficient combustion (PEC) framework was developed to dynamically assign combustion models based on local cost and accuracy metrics in gas-phase reacting flows. In this work, we extend this PEC framework to spray combustion through the rigorous analysis of the multiphase coupling terms in the governing equations. The derivation shows that spray evaporation causes errors in the prediction of species mass fractions for flamelet-based models due to the sensitivity of the local thermo-chemical state to changes in composition caused by fuel vaporization across combustion regimes present in practical spray combustion devices. Sub-model assignment is formulated as a multiple-choice knapsack problem, where computational cost is directly controlled through the fraction of the domain assigned to the FRC sub-model. The extended PEC formulation is applied to the simulation of a realistic rich-quench-lean gas turbine combustor at steady-state conditions, as well as transient operation resulting in lean blow-out (LBO). Analysis of transient simulations during LBO demonstrates the extended PEC formulation’s capacity to dynamically adapt to changing conditions within the combustor. Transient combustor dynamics are shown to approach convergence with limited increases in computational cost, while retaining substantial computational cost reduction compared to monolithic FRC simulations. Through PEC simulations with increasing fractions of the domain assigned to FRC, monolithic flamelet simulations are shown to over-predict flame stability during LBO. Finally, the extended PEC formulation is thus shown to overcome deficiencies of monolithic models by controlling modeling error for multiphase combustion modeling.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2023

Experimental measurement of the rapid mixing of fuel and air in a multi-element diffusion (Hencken) burner

Flat flame burners are valuable tools for studying complex combustion phenomena like particle synthesis due to the well-controlled and well-behaved initial and boundary conditions. In this study, the methane/air flame temperature and tracer density profiles of a multi-element diffusion burner equipped with a central tube were characterized using krypton (Kr) x-ray fluorescence at the Advanced Photon Source at Argonne National Laboratory. The experiments were performed using a fuel-to-oxygen equivalence ratio of 0.8 and 3.3% Kr by volume was added to the reactant gases. One- and two-dimensional scans of non-combusting (i.e., cold flow) and combusting conditions, both with and without flow through the central tube were performed. Spatially-resolved Kr number density profiles were obtained at different planar locations and heights above the surface of the burner. The results show rapid mixing occurred near the exit-plane of the burner and increased spatial uniformity was achieved from 0.1 mm to 0.5 mm above the surface of the burner. Further, the data also showed the impact of the central tube flow on the main methane/air flame, including evidence of the central tube flow mixing with the main flow. Flame temperatures were calculated from the fluorescence measurements at various heights above the burner surface and were in good agreement with model predictions for a burner stabilized flame. These high-fidelity measurements provide accurate baseline data that are particularly valuable for understanding the effects of fuel and air mixing on downstream phenomena, such as the formation of condensed-phase products.

42 ENGINEERING↗

DOE OSTI2023

Reduced chemical kinetic model for CH 4 -air non-premixed flames including excited and charged species

Electric fields can impact small laminar flames by changing their shape and overall behavior by acting on charged species produced in combustion. However, no reduced chemical kinetic model has been developed considering both major species and minor species related to flame characterization and flame behavior in the presence of an electric field. Here, this study presents a reduced chemical kinetic model for methane-air combustion which includes minor excited species (CH*and OH*) and charged species (H 3 O + , HCO + , C 2 H 3 O + , CH 5 O + , O$_{2}^{–}$, OH – , e – , CO$_{3}^{–}$, CHO$_{2}^{–}$, O – , CHO$_{3}^{–}$). The results employing the reduced chemistry model have been validated for a two-dimensional flame geometry by comparison with (i) detailed chemistry simulation results for species location and peak values, and (ii) experimental CH* chemiluminescence location, considering the self-repulsion of charges yet without externally applied electric field to the flame. This reduced chemical kinetic model, with 45 species and 216 reactions, shows a computational demand one-third that of employing its equivalent detailed chemistry (83 species and 394 reactions). The reduction is modest but significant considering that high fidelity is needed to capture the behavior of the chemi-ion and chemiluminescent species. Future works will involve the use of this model for simulations predicting flame behavior with applied electric field (i.e., field strength ≠0 kV/cm).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

In-situ two-dimensional temperature measurements using x-ray fluorescence spectroscopy in laminar flames with high silica particle concentrations

X-ray fluorescence spectroscopy (XRF) was used to measure temperatures and study mixing, for the first time in silica particle synthesis flames. Hexamethyldisiloxane (HMDSO) and trimethylsilanol (TMSO) were the particle precursors. A multi-element diffusion burner was used to produce a flat methane flame, and the precursors, dilute in inert gas, were injected via a central jet. Krypton was the fluorescent medium at 3.2 % concentration by volume. Scans with Kr in the central flow and not in the main flow were made to assess the mixing effects between the central and main gas flows. When HMDSO or TMSO were added, a secondary diffusion flame formed between the jet and the main methane flame. The results revealed a dramatic change in the centerline temperature profile of the jet gases when HMDSO or TMSO were added. The main methane flame stoichiometry also affected the temperature profiles. The results show HMDSO and TMSO reactions are initiated in a lowtemperature and low-oxygen concentration region of the jet where thermal decomposition is not expected to be significant. Reaction of the particle precursors is therefore attributed to radical transport from the main methane flame. In the current work, particle number densities of up to 270 g/m 3 locally are estimated. Thus, the study also demonstrates the capability of the XRF technique for high spatial fidelity measurements in flames with high concentrations of condensed-phase particles, leveraging the attribute that the XRF signal is generally not impacted by condensed-phase interferences. In conclusion, the observations and data obtained in this study inform likely reaction pathways for this important class of siloxane compounds.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Computational diagnostics and characterization of combustion recession in diesel sprays

While low-temperature combustion (LTC) strategies have been found to mitigate nitrogen oxides and particulate matter emissions in diesel engines, studies have also associated LTC with an increase in unburnt hydrocarbons. With more recent studies on diesel after end-of-injection (AEOI), combustion recession is identified as a phenomenon where at near nozzle region, high-temperature ignition (HTI) combustion can propagate back to the nozzle tip consuming the unburnt hydrocarbons AEOI. Current literature has suggested that combustion recession is controlled by auto-ignition. However, high-fidelity simulations and detailed analysis of such a mechanism are missing. In this study, comprehensive Large Eddy Simulations of a reacting spray at “Spray A” conditions are performed, where detailed analysis of combustion recession concerning flame morphology and propagation modes are included. In particular, this study demonstrated for the first time that while combustion recession is mainly auto-ignition dominated (consistent with the literature), a cool flame was found to deflagrate towards the richer regions of the mixture, promoting mixing and increasing the mixture temperature. This leads to HTI kernels, which then grow and develop as deflagrative waves, therefore sustaining the combustion recession process. The study also detailed the extinction mechanism of combustion: the entrainment wave will overlean the near-nozzle mixtures, rendering it unable to support HTI, which leads to the extinction of the upstream flame AEOI in lower reactivity mixtures. Combustion recession is also observed to be contingent on the chemical and diffusion processes, even at low scalar dissipation rates. Finally, a new criterion for combustion recession based on chemical explosive mode is proposed and validated with previous combustion recession index to quantify the extent of HTI in near-nozzle mixtures AEOI. The newly developed metric combined with a previous experimentally-based metric can provide simple but valuable measurements of the degree and propensity of the upstream flame AEOI.

CEMA↗

DOE OSTI2025

A theoretical kinetic study of ĊH 3 + ṄH 2 : From electronic structure to NH 3 /CH 4 combustion modelling implications

Carbon–nitrogen interaction reactions play an important role in governing the reactivity of ammonia blended fuels. However, there remains uncertainties regarding their detailed reaction pathways and rate constants, hampering the development of high-fidelity chemical kinetic models. In this study, the kinetics of ĊH 3 + ṄH 2 , a key C–N interaction reaction in ammonia/methane blend combustion have been investigated. The potential energy surface has been explored using the high-level ANL0F method, yielding highly accurate stationary point energies that agree with ATcT values within 0.1 kcal mol –1 . Variable reaction coordinate transition state theory is used to treat the barrierless association and decomposition reaction channels, based on directly sampled radical-radical interaction energies at the CASPT2-F12(2e,2o)/cc-pVTZ-F12 level of theory. The minimum transitional mode numbers of states obtained are then coupled with the RRKM/master equation to calculate temperature- and pressure-dependent rate constants. Our a priori calculations capture available experimental measurements from the literature very well. The calculated rate constants have been incorporated into an NH 3 /CH 4 chemical kinetic model currently under development at the University of Galway. The effect of the updated kinetic data for ĊH 3 + ṄH 2 on model predicted NH 3 /CH 4 fuel reactivity is elucidated.

ab initio↗

DOE OSTI2025

C3MechLite: An integrated component library of compact kinetic mechanisms for low-carbon, carbon neutral and zero-carbon fuels

Based on our latest detailed chemical reaction mechanism, C3MechV4.0, we have developed two reduced reaction mechanisms—C3MechLite and C3MechCore—targeting C 0 –C 3 chemical species including NH 3 . C3MechLite (61 species), contains a number of species comparable to GRI-Mech (53 species), that can accurately predict the combustion characteristics of hydrogen, carbon monoxide, ammonia, methane, natural gas, nitrogen oxides, and their mixtures for a wide range of conditions. C3MechCore (118 species) targets a more comprehensive range of C 0 –C 3 fuels, including ammonia, methanol, ethanol, and dimethyl ether. Both mechanisms demonstrate predictive accuracy comparable to C3MechV4.0 for the combustion characteristics of the target fuels. C3MechLite is designed with a component library structure, enabling further reduction in mechanism size depending on the fuel(s) of interest for 2D/3D numerical simulations. Various combinations of component libraries were validated, and the average prediction error remains within 1 % compared to C3MechLite. Furthermore, the mechanism was applied to 3D LES simulations of H 2 lifted flames and was confirmed to reproduce flame characteristics with high accuracy. C3MechLite and its component library structure enable high-fidelity and computationally efficient chemical kinetic mechanisms, paving the way for application in more complex combustion simulations.

Ammonia↗

DOE OSTI2022

A machine learning approach to predict thermal expansion of complex oxides

Although it is of scientific and practical importance, the state-of-the-art of predicting the thermal expansion of oxides over broad temperature and composition ranges by physics-based atomistic simulations is currently limited to qualitative agreements. We present an emerging machine learning (ML) approach to accurately predict the thermal expansion of cubic oxides with a dataset consisting of experimentally measured lattice parameters while using the metal cation polyhedron and temperature as descriptors. High-fidelity ML models that can accurately predict temperature- and composition-dependent lattice parameters of cubic oxides with isotropic thermal expansions have been successfully trained. The ML-predicted thermal expansions of oxides not included in the training dataset have shown good agreement with available experiments. The limitations of the current approach and challenges to go beyond cubic oxides with isotropic thermal expansion are also briefly discussed.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Accelerated computation of lattice thermal conductivity using neural network interatomic potentials

We report with the development of the density functional theory (DFT) and ever-increasing computational capacity, an accurate prediction of lattice thermal conductivity based on the Boltzmann transport theory becomes computationally feasible, contributing to a fundamental understanding of thermal conductivity as well as a choice of the optimal materials for specific applications. However, steep costs in evaluating third-order force constants limit the theoretical investigation to crystals with high symmetry and few atoms in the unit cell. Currently, machine learning potentials are garnering attention as a computationally efficient high-fidelity model of DFT, and several studies have demonstrated that the lattice thermal conductivity could be computed accurately via the machine learning potentials. However, test materials were mostly crystals with high symmetries, and the applicability of machine learning potentials to a wide range of materials has yet to be demonstrated. Furthermore, establishing a standard training set that provides consistent accuracy and computational efficiencies across a wide range of materials would be useful. To address these issues, herein we compute lattice thermal conductivities at 300 K using neural network interatomic potentials. As test materials, we select 25 materials with diverse symmetries and a wide range of lattice thermal conductivities between 10 -1 and 10 3 Wm -1 K -1 . Among various choices of training sets, we find that molecular dynamics trajectories at 50–700 K consistently provide results at par with DFT for the test materials. In contrast to pure DFT approaches, the computational cost in the present approach is uniform over the test materials, yielding a speed gain of 2–10 folds. When a smaller reduced training set is used, the relative efficiency increases by up to ~50 folds without sacrificing accuracy significantly. The current work will broaden the application scope of machine learning potentials by establishing a robust framework for accurately computing lattice thermal conductivity with machine learning potentials.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Recent progress on the mesoscale modeling of architected thin-films via phase-field formulations of physical vapor deposition

Thin-film coatings can be found everywhere in modern technological applications due to desirable electrical, mechanical, chemical, and optical properties. These properties directly depend upon the thin-film’s microstructural features, which are themselves influenced by the materials and vapor-deposition processing conditions used for fabrication. As such, understanding processing-microstructure relationships is essential to designing thin-films with optimized properties, and discovering new processing conditions that allow for novel thin-films with multifunctional microstructures. Here, a short review is presented on recent developments that utilize the phase-field method to simultaneously model the vapor-deposition process and corresponding microstructure formation at the mesoscale. Also phase-field-based vapor-deposition models that simulate thin-film growth of immiscible alloy and polycrystalline systems are highlighted in addition to machine-learning-based surrogate models that can facilitate accelerated high-fidelity simulations along with materials design and exploration studies.

36 MATERIALS SCIENCE↗

DOE OSTI2022

GrainNN: A neighbor-aware long short-term memory network for predicting microstructure evolution during polycrystalline grain formation

High fidelity simulations of grain formation in alloys are an indispensable tool for process-to-mechanical-properties characterization. Such simulations, however, can be computationally expensive as they require fine spatial and temporal discretizations. Their cost becomes an obstacle to parametric studies and ensemble runs and ultimately makes downstream tasks like optimal control and uncertainty quantification challenging. To enable such downstream tasks, we introduce GrainNN, an efficient and accurate reduced-order model for epitaxial grain growth in additive manufacturing conditions. GrainNN is a sequence-to-sequence long-short-term-memory (LSTM) deep neural network that evolves the dynamics of manually crafted features. Its innovations are (1) an attention mechanism with grain-microstructure-specific transformer architecture; and (2) an overlapping combination of several clones of the network to generalize to grain configurations that are different from those used for training. This design enables GrainNN to predict grain formation for unseen physical parameters, grain number, domain size and geometry. Furthermore, GrainNN not only reconstructs the quantities of interest but also can be pointwise accurate. In our numerical experiments, we use a polycrystalline phase field method to both generate the training data and assess GrainNN. For multiparametric, ensemble simulations with many grains, GrainNN can be orders of magnitude faster than phase field simulations, while delivering 5%–15% pointwise error. Additionally, this speedup includes the cost of the phase field simulations for generating training data.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Machine learning based approach to predict ductile damage model parameters for polycrystalline metals

Damage models for ductile materials typically need to be parameterized, often with the appropriate parameters changing for a given material depending on the loading conditions. This can make parameterizing these models computationally expensive, since an inverse problem must be solved for each loading condition. Using standard inverse modeling techniques typically requires hundreds or thousands of high-fidelity computer simulations to estimate the optimal parameters. Additionally, the time of a human expert is required to set up the inverse model. Machine learning has recently emerged as an alternative approach to inverse modeling in these settings, where the machine learning model is trained in an offline manner and new parameters can be quickly generated on the fly, after training is complete. Here, this work utilizes such a workflow to enable the rapid parameterization of a ductile damage model called TEPLA with a machine learning inverse model. The machine learning model can efficiently estimate the model parameters much faster, as compared to previously employed methods, such as Bayesian calibration. The results demonstrate good accuracy on a synthetic test dataset and is validated against experimental data.

36 MATERIALS SCIENCE↗