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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.

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At least 1,207 records · Page 67
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Flame kinetics at scramjet-engine-relevant conditions: Role of prompt dissociation of weakly-bound radicals

Combustion in high-speed ram-based propulsion engines occurs under distinct thermodynamic conditions of high reactant temperatures (greater than 1000 K) and relatively low pressures (<5 atm). There is a lack of fundamental flame measurements at such conditions that result in adiabatic flame temperatures (T ad ) exceeding 2500 K. In this work, we have measured laminar flame speeds of oxygen-enriched CH 4 /oxidizer mixtures at sub-atmospheric conditions to probe kinetics at high T ad using the isobaric spherically expanding flame approach. Simulations with recent kinetic models revealed increasing differences between data and model predictions with increasing T ad , reaching up to 25 %. Kinetic analyses reveal that at the thermodynamic conditions in these O 2 -enriched flames, i.e., lower pressures and higher T ad , the effects of HCO prompt dissociation are accentuated. In addition to HCO, the prompt dissociations of CH 2 OH and C 2 H 5 are also considered. Here, the prompt dissociations of all three radicals were evaluated and their effects considered in flame speed simulations. Reaction path analysis for the present flames revealed that approximately half of the reaction flux for HCO formation undergoes prompt dissociation to H + CO. Furthermore, these analyses also revealed that the pathways and sensitive reactions are similar between oxygen-enriched fuel/oxidizer mixtures and preheated fuel/air mixtures, if both have similar T ad . Thus, flames of oxygen-enriched mixtures could be a surrogate to probe the flame chemistry of highly preheated mixtures at relatively low pressures that are often encountered in ram-based propulsion engine combustors.

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

A combined machine learning and density functional theory study of binary Ti-Nb and Ti-Zr alloys: Stability and Young’s modulus

The multicomponent Ti alloys, specifically the -phase, have experienced a strong growth over the last decades, due to their outstanding properties of ultra-high strength and low Young’s modulus. These properties play a significant role in many aerospace and biomedical applications. Selection and optimization of multicomponent alloys is challenging due to the vast chemical and compositional space. Here we investigate the use of machine learning techniques informed by density functional calculations to guide the selection of Nb- and Zr-based Ti binary alloys. From the cubic structures obtained from high throughput calculations and literature, we identify several structures with Young’s moduli below 40 GPa. The multivariant decision tree methods provide efficient surrogate models to identify structure variables have high influences on the energetic stability and Young’s modulus. Furthermore, we implement a workflow of incorporating DFT provided results and machine learning method to explore the chemical and composition space of other binary and multicomponent alloys, to eventually accelerate the material design via taking advantages of identified key variables.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Machine learning in materials science: From explainable predictions to autonomous design

The advent of big data and algorithmic developments in the field of machine learning (and artificial intelligence, in general) have greatly impacted the entire spectrum of physical sciences, including materials science. Materials data, measured or computed, combined with various techniques of machine learning have been employed to address a myriad of challenging problems, such as, development of efficient and predictive surrogate models for a range of materials properties, screening and down-selection of novel candidate materials for targeted applications, new methodologies to improve and further expedite molecular and atomistic simulations, with likely many more important developments to come in the foreseeable future. While the applications thus far have provided a glimpse of the true potential data-enabled routes have to offer, it has also become clear that further progress in this direction hinges on our ability to understand, explain and rationalize findings of a machine learning model in light of the domain-knowledge. This focused review provides an overview of the main areas where machine learning has been widely and successfully used in materials science. Subsequently, a brief discussion of several techniques that have been helpful in extracting physically-meaningful insights, causal relationships and design-centric knowledge from materials data is provided. Finally, we identify some of the imminent opportunities and challenges that materials community faces in this exciting and rapidly growing field.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Graph neural networks predict energetic and mechanical properties for models of solid solution metal alloy phases

Here, we developed a PyTorch-based architecture called HydraGNN that implements graph convolutional neural networks (GCNNs) to predict the formation energy and the bulk modulus for models of solid solution alloys for various atomic crystal structures and relaxed volumes. We trained the GCNN surrogate model on a dataset for nickel–niobium (NiNb) generated by the embedded atom model (EAM) empirical interatomic potential for demonstration purposes. The dataset was generated by calculating the formation energy and the bulk modulus as a prototypical elastic property for optimized geometries starting from initial body-centered cubic (BCC), face-centered cubic (FCC), and hexagonal compact packed (HCP) crystal structures, with configurations spanning the possible compositional range for each of the three types of initial crystal structures. Numerical results show that the GCNN model effectively predicts both the formation energy and the bulk modulus as function of the optimized crystal structure, relaxed volume, and configurational entropy of the model structures for solid solution alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Microstructure modeling of nuclear structural materials: Recent progress and future directions

Modeling and simulation of microstructures are essential to understand the complex responses and behaviors of nuclear materials in extreme environments. The needs to assess the extended life operation as well as the growing interest in accelerating nuclear materials development and qualification have stimulated the use of high-fidelity multiscale models aided by empirical and ab initio data. This paper reviews the role of various models across different length and time scales in investigating irradiation effects on microstructure evolution and degradation, in particular the embrittlement caused by radiation induced or enhanced formation of nanoscale chemical heterogeneities. The strength and limitations of these models, including classical rate theories, cluster dynamics, phase-field methods, and atomistic models informed by ab initio energies, are discussed with seminal examples. Challenges regarding the lack of thermo-kinetic data and theoretical treatments considering chemical complexities and magnetic excitations, as well as the stabilizing effect by excess point defects in nuclear structural materials are presented, along with potential solutions based on ab initio informed surrogate energy models and statistical sampling by Monte Carlo simulations. Further, the review then highlights the opportunities to leverage the advantages of different methods by establishing hybrid models by shared variables or coupled codes and applications. Finally, the review concludes with forward-looking remarks on how the use of physics-based models can aid the improvement of machine-learning models of property degradation and vice versa.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Ensemble cure kinetics network (ECK-Net): A method to derive cure kinetics of thermosetting resin

This paper introduces an Ensemble Cure Kinetics Network (ECK-Net), a neural network (NN)–based framework for modeling the cure kinetics of thermosetting resins within a phenomenological context. ECK-Net replaces traditional analytic models, which require extensive chemical insight and multiple isothermal/non-isothermal experiments, with a data-driven surrogate that maps nonlinear relationships between temperature, degree of cure, and reaction rate from differential scanning calorimetry data. The proposed approach predicts input-dependent kinetic coefficients of a generalized nth-order reaction equation rather than reaction rates directly, enabling a single unified model to represent various epoxy systems without relying on iso-conversional analysis or predefined functional forms. To ensure robustness, multiple independently trained networks under different random initializations are blended through an ensemble strategy, effectively mitigating the stochastic variability inherent to neural networks. The framework is validated using experimental datasets from multiple resin systems, including aerospace-grade materials (Toray 3900-2, Cycom 5320-1, and Hexcel 8552) and a windmill-grade resin (RIMR 035c). The model accurately reproduces the temporal evolution of the degree of cure under manufacturers’ recommended cure cycles across all tested resins systems, yielding Pearson’s correlation coefficients of 0.992, 0.994, 0.993, 0.997, respectively. To demonstrate process-level applicability, the trained network was implemented within the Abaqus environment to simulate out-of-autoclave (OOA) curing process of the CFRP panel composed of Toray T830H-6K/3900-2D prepreg. The simulation results showed excellent agreement with experimental temperature response (maximum peak temperature, simulation: 189.6 °C, experiment: 188.5 °C) and the final degree of cure (simulation: 0.948, experiment: 0.960 ± 0.013), confirming ECK-Net’s capability as a reliable alternative to conventional cure kinetics modeling methods.

Composite curing↗
DOE OSTI2025

Damage progression and failure of SiC/SiC composite tubes under hard-contact radial expansion

The response of silicon carbide (SiC) fiber-reinforced SiC matrix (SiC/SiC) composite cladding to mechanical interaction with fissile fuel is a knowledge gap that must be overcome to design and assess SiC-based cladding systems for advanced nuclear applications. This study developed the relevant mechanical testing capability and identified the failure behavior and the critical microstructural features and processing defects. Sections of SiC composite tube were subjected to a modified expansion-due-to-compression (EDC) test in an X-ray computed tomography microscope: a polyurethane plug pressed surrogate Al 2 O 3 into the inner walls of the SiC/SiC composite tubes to achieve hard contact. A pure EDC test with just a polyurethane plug was also performed as a reference. Through the use of displacement fields, digital volume correlation revealed inhomogeneous deformation fields in the tubes, even for pure EDC, which was related to the inherent defects in the structure. Deep learning–aided segmentation and systematic data analysis revealed that the presence of inhomogeneous deformation applied by the hard contact was exaggerated by the presence of inner surface imperfections left behind from the matrix densification process. In conclusion, the findings provide insights into the applications, highlighting the necessity for improvements in inner surface roughness and the incorporation of localized contacts in pellet–cladding mechanical interaction computational models.

Composites↗
DOE OSTI2024

Multifidelity methods for uncertainty quantification of a nonlocal model for phase changes in materials

This study is devoted to the construction of a multifidelity Monte Carlo (MFMC) method for the uncertainty quantification of a nonlocal, non-mass-conserving Cahn-Hilliard model for phase transitions with an obstacle potential. Here, we are interested in estimating the expected value of an output of interest (OoI) that depends on the solution of the nonlocal Cahn-Hilliard model. As opposed to its local counterpart, the nonlocal model captures sharp interfaces without the need for significant mesh refinement. However, the computational cost of the nonlocal Cahn-Hilliard model is higher than that of its local counterpart with similar mesh refinement, inhibiting its use for outer-loop applications such as uncertainty quantification. The MFMC method augments the desired high-fidelity, high-cost OoI with a set of lower-fidelity, lower-cost OoIs to alleviate the computational burden associated with nonlocality. Most of the computational budget is allocated to sampling the cheap surrogate models to achieve speedup, whereas the high-fidelity model is sparsely sampled to maintain accuracy. For the non-mass-conserving nonlocal Cahn-Hilliard model, the use of the MFMC method results in, for a given computational budget, about an order of magnitude reduction in the mean-squared error of the expected value of the OoI relative to that of the Monte Carlo method.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Role of electronic energy loss on defect production and interface stability: Comparison between ceramic materials and high-entropy alloys

High-entropy alloys (HEAs) and some complex alloys exhibit desirable properties and significant structural stability in harsh environments, including possible applications in advanced reactors. Energetic ion irradiation is often used as a surrogate for neutron irradiation; however, the impact of ion electronic energy deposition and dissipation is often neglected. Moreover, differences in recoil energy spectrum and density of cascade events on damage evolution must also be considered. In many chemically complex alloys, the mean free path of electrons is reduced significantly, thus their decreased thermal conductivity and slow dissipation of localized radiation energy can have noticeable effects on displacement cascade evolution that is greatly different from metals with high thermal conductivity. In this work, nanocrystalline HEAs of Ni 20 Fe 20 Co 20 Cr 20 Cu 20 and nonequiatomic (NiFeCoCr) 97 Cu 3 , both having much lower room-temperature thermal conductivity than pure Ni or Fe, are chosen as model HEAs to reveal the role that electronic energy loss during ion irradiation has in complex alloys. The response of nanocrystalline HEAs is investigated under irradiation at room temperature using MeV Ni and Au ions that have different ratios of electronic energy to damage energy, which is the energy dissipated in displacing atoms. Different from previously reported amorphization of nanocrystalline SiC, experimental results on these HEAs show that, similar to the process in nanocrystalline oxide materials, both inelastic thermal spikes via electron–phonon coupling and elastic thermal spikes via collisions among atomic nuclei contribute to the overall grain growth. The growth follows a power law dependence with the total deposited ion energy, and the derived value of the power-exponent suggests that the irradiation-induced instability at and near grain boundaries leads to local rapid atomic rearrangements and consequently grain growth. The high power-exponent value can be attributed to the sluggish diffusion and delayed defect evolution arising from the chemical complexity intrinsic to HEAs. Here, this work calls attention to quantified fundamental understanding of radiation damage processes beyond that of simplified displacement events, especially in simulating neutron environments.

36 MATERIALS SCIENCE↗
DOE OSTI2026

The high explosives & affected targets (HEAT) dataset

Artificial Intelligence (AI) surrogate models offer a computationally efficient alternative to full-physics simulations, yet no existing datasets are publicly available for training, testing, and validation of machine learning models of the dynamics of high-explosive driven shocks through multiple materials. Shock propagation through materials is a computationally challenging problem because simulations must include material-specific equations of state (EOS) along with descriptions of other physical processes such as plastic deformation, phase change, damage processes, fluid instabilities, and multi-material interactions. Shocks are typically initiated by high-velocity impacts or explosive loading. The latter case necessitates the addition of models of reactive materials to represent high-explosive (HE) detonation. Here, to address the lack of an expansive dataset for multi-material shock propagation in the AI/ML community, we present the High-Explosives and Affected Targets (HEAT) Dataset. HEAT is a physics-rich collection of two-dimensional, cylindrically symmetric, simulations generated using an Eulerian, multi-material, shock-propagation code developed at Los Alamos National Laboratory. The dataset includes two partitions: (1) the expanding shock-cylinder (CYL) simulations, Figs. 1, and (2) the Perturbed Layered Interface (PLI) simulations, Fig. 2. Entries in both partitions consist of time series of arrays of thermodynamic fields (pressure, density, and temperature), kinematic fields (position and velocity), and additional fields that depend on thermodynamic and/or kinematic fields (e.g., material stress). Materials in the CYL partition include solids (aluminium, copper, depleted uranium, stainless steel, tantalum, and a generic polymer), a liquid (water), gases (air, nitrogen), and a generic detonating material (high explosive, HE). The PLI partition spans a highly varying geometry but consists of fixed materials across entries: Copper, aluminium, stainless steel, generic polymer, and generic HE. HEAT captures critical phenomena such as momentum transfer, shock propagation, plastic deformation, and thermal effects, making HEAT a valuable benchmark for development of AI/ML emulation of multi-material shock propagation.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A deep learning-based Bayesian framework for high-resolution calibration of building energy models

Calibrating building energy models (BEMs), i.e., closing discrepancy between modeling and field measurements, is of significance to support its applications in building sustainability and resilience analysis. However, as being widely used in practice, current Bayesian calibration is mostly performed in low-resolution (annual or monthly), instead of high-resolution (hourly or sub-hourly), which is crucial to support emerging BEM applications, such as building-renewable energy integration (demand response) and smart control. This is attributable to the gaps in current Bayesian calibration process, including (1) difficulty in supporting reliable high-resolution calibration with over-parameterization and multi-solution issues, (2) inadequacy of meta-model to capture temporal building dynamics in high-resolution, and (3) excessive computational burdens of covariance matrix calculation in Bayesian inference. Therefore, to close these gaps, this research proposes a novel deep learning-based Bayesian calibration framework, involving pre-calibration mechanism, Long Short-Term Memory as surrogate models, and simplified covariance matrix calculation, to calibrate BEMs in high temporal resolution (i.e., hourly) with enhanced accuracy and computational efficiency. Finally, the case study demonstrates its effectiveness to match modeling outcomes with measurements and realize CV-RMSE of < 30 % and NMBE of < 6 % in hourly resolution, as well as a significant reduction of calibration time (by > 99 %, from > 600 h to ~ 1.5 h).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Neural message-passing for objective-based uncertainty quantification and optimal experimental design

Various real-world scientific applications involve the mathematical modeling of complex uncertain systems with numerous unknown parameters. Accurate parameter estimation is often practically infeasible in such systems, as the available training data may be insufficient and the cost of acquiring additional data may be high. In such cases, based on a Bayesian paradigm, we can design robust operators retaining the best overall performance across all possible models and design optimal experiments that can effectively reduce uncertainty to enhance the performance of such operators maximally. While objective-based uncertainty quantification (objective-UQ) based on MOCU (mean objective cost of uncertainty) provides an effective means for quantifying uncertainty in complex systems, the high computational cost of estimating MOCU has been a challenge in applying it to real-world scientific/engineering problems. In this work, we propose a novel scheme to reduce the computational cost for objective-UQ via MOCU based on a data-driven approach. We adopt a neural message-passing model for surrogate modeling, incorporating a novel axiomatic constraint loss that penalizes an increase in the estimated system uncertainty. As an illustrative example, we consider the optimal experimental design (OED) problem for uncertain Kuramoto models, where the goal is to predict the experiments that can most effectively enhance robust synchronization performance through uncertainty reduction. We show that our proposed approach can accelerate MOCU-based OED by four to five orders of magnitude, without any visible performance loss compared to the state-of-the-art. The proposed approach applies to general OED tasks, beyond the Kuramoto model.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Water reactive polyurethane grouting for deep vadose zone contaminant immobilization

Contaminants in the deep vadose zone (DVZ) pose a long-term threat to groundwater, human health, and the environment. Polyurethane grouting is a type of chemical grouting that is potentially advantageous in immobilizing contaminants in DVZs. Polyurethane resin has low viscosity that makes it feasible to penetrate and fill the pore space between fine particles. Here, in this study a series of laboratory pressure grouting and leaching tests were conducted to simulate and assess the effectiveness of polyurethane grouting for immobilizing contaminants in DVZs. Soil with fine particles was prepared with 127 I (as iodide) that served as a non-radioactive surrogate for radioactive 129 I. After grouting and curing, leaching tests were used to measure and compare contaminant diffusivity and leachability index values. Additionally, changes in porosity and saturated hydraulic conductivity of grouted soil were measured. X-Ray Computer Tomography (XCT) results showed that the cured polyurethane was distributed nearly homogeneously and approximately half of the voids were filled with cured polyurethane. Grouting reduced the saturated hydraulic conductivity of soil by 37 %. The effective diffusivity decreased by more than 80 % as compared with the ungrouted soil. The leachability index of the grouted soil was 6.5; meeting the criteria established by the U.S. Nuclear Regulatory Commission (NRC) standard. The results obtained in this study provide a valuable assessment of polyurethane grouting for iodide immobilization in the DVZ and indicate this approach may be a viable method for contaminant remediation in DVZ soils.

Contaminant Diffusion↗
DOE OSTI2020

Chemical changes in thirdhand smoke associated with remediation using an ozone generator

Ozonation is a common remediation approach to eliminate odors from mold, tobacco and fire damage in buildings. Little information exists to: 1) assess its effectiveness; 2) provide guidance on operation conditions; and 3) identify potential risks associated with the presence of indoor ozone and ozonation byproducts. The goal of this study is to evaluate chemical changes in thirdhand smoke (THS) aerosols induced by high levels of ozone, in comparison with THS aerosols aged under similar conditions in the absence of ozone. Samples representing different stages of smoke aging in the absence of ozone, including freshly emitted secondhand smoke (SHS) and THS, were collected inside an 18-m 3 room-sized chamber over a period of 42 h after six cigarettes were consumed. The experiments involved collection and analysis of gas phase species including volatile organic compounds (VOCs), volatile carbonyls, semivolatile organic compounds (SVOCs), and particulate matter. VOC analysis was carried out by gas chromatography/mass spectrometry with a thermal desorption inlet (TD-GC/MS), and volatile carbonyls were analyzed by on-line derivatization with dinitrophenylhydrazine (DNPH), followed by liquid chromatography with UV/VIS detection. SVOCs were extracted from XAD-coated denuders and Teflon-coated fiberglass filters in the absence of ozone. In those extracts, tobacco-specific nitrosamines (TSNAs) and other SVOCs were analyzed by gas chromatography with positive chemical ionization-triple quadrupole mass spectrometric detection (GC/PCI-QQQ-MS), and polycyclic aromatic hydrocarbons (PAHs) were quantified by gas chromatography with ion trap mass spectrometric detection (GC/IT-MS) in selected ion monitoring mode. Particulate matter concentration was determined gravimetrically. In a second experiment, a 300 mg h -1 commercial ozone generator was operated during 1 h, one day after smoke was generated, to evaluate the remediation of THS by ozonation. VOCs and volatile carbonyls were analyzed before and after ozonation. Extracts from fabrics that were exposed in the chamber before and after ozonation as surrogates for indoor furnishings were analyzed by GC/IT-MS, and aerosol size distribution was studied with a scanning mobility particle sizer. Ozone concentration was measured with a photometric detector. An estimated 175 mg ozone reacted with THS after 1 h of treatment, corresponding to 58% of the total O 3 released during that period. Fabric-bound nicotine was depleted after ozonation, and the surface concentration of PAHs adsorbed to fabric specimens decreased by an order of magnitude due to reaction with ozone, reaching pre-smoking levels. These results suggest that ozonation has the potential to remove harmful THS chemicals from indoor surfaces. However, gas phase concentrations of volatile carbonyls, including formaldehyde, acetaldehyde and acetone were higher immediately after ozonation. Ultrafine particles (UFP, in most cases with size <60 nm) were a major ozonation byproduct. UFP number concentrations peaked shortly after ozonation ended, and remained at higher-than background levels for several hours. Based on these results, minimum re-entry times after ozone treatment were predicted for different indoor scenarios. Clearly defining re-entry times can serve as a practical measure to prevent acute exposures to ozone and harmful ozonation byproducts after treatment. This study evaluated potential benefits and risks associated with THS remediation using ozone, providing insights into this technology.

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

Physics-informed heterogeneous graph neural networks for DC blocker placement

The threat of geomagnetic disturbances (GMDs) to the reliable operation of the bulk energy system has spurred the development of effective strategies for mitigating their impacts. One such approach involves placing transformer neutral blocking devices, which interrupt the path of geomagnetically induced currents (GICs) to limit their impact. The high cost of these devices and the sparsity of transformers that experience high GICs during GMD events, however, calls for a sparse placement strategy that involves high computational cost. To address this challenge, we developed a physics-informed heterogeneous graph neural network (PIHGNN) for solving the graph-based dc-blocker placement problem. Our approach combines a heterogeneous graph neural network (HGNN) with a physics-informed neural network (PINN) to capture the diverse types of nodes and edges in ac/dc networks and incorporates the physical laws of the power grid. We train the PIHGNN model using a surrogate power flow model and validate it using case studies. Results demonstrate that PIHGNN can effectively and efficiently support the deployment of GIC dc-current blockers, ensuring the continued supply of electricity to meet societal demands. Furthermore, our approach has the potential to contribute to the development of more reliable and resilient power grids capable of withstanding the growing threat that GMDs pose.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Experimental investigation of unsteady flow dynamics in therminol-66 using edge tone-induced oscillations

Recent research has demonstrated that flow oscillations induced by a wedge positioned opposite the jet outlet in edge tones can be effectively utilized for flow measurement purposes. This highlights the necessity for developing advanced instrumentation capable of precise flow measurements in experimental setups exposed to radiation and in challenging fluids such as molten salts. Here, this study contributes to the existing body of knowledge by conducting experiments using Therminol-66, an effective surrogate for FLiBe under appropriate temperature conditions for nuclear applications, across a broad range of Reynolds numbers from 17,700 to 555,000. Experiments were conducted with Therminol-66 at approximately 368.8 K under atmospheric pressure, with varying flow rates. A reliable correlation was established between the generated frequencies, corresponding flow rates, and device geometry parameters. The data were subjected to non-dimensional analysis to reveal the interrelationships among flow rate, fluid properties, device geometry, and their impact on the acoustic tones produced by the edge tone device. These findings contribute to the development of precise flow measurement instrumentation for use in challenging environments.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2020

Assessment of various tar and soot treatment methods and a priori analysis of the steady laminar flamelet model for use in coal combustion simulation

Soot formed in the fuel-rich regions of coal flames is known to have a significant impact on the radiative heat transfer characteristics of the flame. It is therefore important to account for the effect of soot radiation when simulating coal combustion systems. Several recent coal combustion studies have used acetylene as a surrogate for devolatilized tar and neglect soot formation altogether, while others have utilized empirical models for tar and soot or even chemical mechanisms which include tar and soot as species. In this work, we investigate several methods of treating coal-derived tar and soot and the impact each treatment has on predicted furnace temperature, particle mass loss, and other quantities of interest. Furthermore, we perform a priori comparisons of simulation results to calculations from a steady laminar flamelet model in order to assess the accuracy of a flamelet approach for each tar and soot treatment considered. Data used as a basis for the study is generated through One-Dimensional Turbulence (ODT) simulation of a coal combustion system in which high- fidelity models are used for gas phase chemistry and devolatilization, where char combustion is neglected.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Unveiling stimulation fluid-driven alterations in shale pore architecture through combined interpretation of TD-NMR and multi-component gas adsorption

The hydraulic fracturing stimulation process can alter petrophysical and flow properties of the shale matrix in unconventional reservoirs. These alterations can result from interactions with native and exogenous fluids, and can be detrimental to reservoir productivity. Different experimental procedures have been proposed to study reactive processes and thereby understand the aforementioned interactions. Most published works do not consider the rock initial saturation with formation fluid, or the effect of organic matter (OM). To gain insights into these effects, two reactive experimental sets, differing in experimental conditions, were conducted. In the first set, at 125°C and 45 MPa, a stimulation fluid surrogate was put in contact with Baxter Shale samples, previously saturated and equilibrated with synthetic formation fluid. A baseline consisted of exposure to formation fluid exclusively. In the second set, at 0.08 MPa and 25°C, contact with stimulation fluid took place as in the first set. Two additional assays in this second set either skipped the initial formation fluid saturation or removed the OM, respectively. Petrophysical properties analyses, focused on the pore architecture, relied on the combined interpretation of gas adsorption and Time-Domain Nuclear Magnetic Resonance (TD-NMR). Results show that the presence of the formation fluid significantly restricts pore space accessibility to the stimulation fluid and mitigates the pore-architecture alteration to the macro-pore region. Mineral dissolution and precipitation dominate over surface or wettability alteration. Next, results suggest that the OM restricts accessibility to some pores. Ultimately, the equilibration process with synthetic formation fluid drives mainly surface alteration, unhindered by OM.

58 GEOSCIENCES↗