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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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Markovian transport processes in a heterogeneous, variably saturated watershed: A multi-domain spatial Markov model

Transport processes in watersheds remain challenging to describe accurately with upscaled models because of their intricate complexities at multiple scales that can lead to multi-modal, non-Fickian breakthrough curves. One of the recent advances in solute transport modeling has been the Lagrangian spatial Markov model (SMM), which describes transport using velocity increments and models the correlated transitions between them. The method has been applied previously to saturated porous media and fractured media, but these successful applications suggest it may be useful for describing watershed scale processes as well. An existing, 3-D, heterogeneous variably saturated model of a headwaters catchment and classical random walk particle tracking were used to generate a forward simulation and the resulting transport was analyzed with a spatial Markov framework. Existing SMM methods were used to analyze the transport paths through the complex, unconfined domain and we found that the vadose zone plays an important role in the correlation structure. As such, a new kind of SMM is proposed, termed a multi-domain SMM (MD-SMM), that uses separate correlation models for auto-transitions in the vadose zone and the saturated zone, and for the cross-transitions between them. The MD-SMM improves representation of transport relative to the single domain model and can naturally delineate times when a particle is in the vadose zone or the saturated zone, adding an additional degree of realism to the upscaled model. Lastly, we show that the transition probability matrix for transport in the saturated portion of this unconfined system is approximately Markovian, which is an important validation of the proposed framework.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Neutron transport methods for multiphysics heterogeneous reactor core simulation in Griffin

Griffin is a reactor physics application based on the Multiphysics Object-Oriented Simulation Environment (MOOSE). This work discloses the methods, algorithms, and implementation for simulating heterogeneous reactor dynamics models. Griffin utilizes a discontinuous finite-element method with discrete ordinates (DFEM-S ) to discretize the field variable of the multigroup neutron transport equation. Multiphysics feedback is handled using two-step tabulated cross-section methodology. Feedback quantities are evaluated using the MOOSE-MultiApp system to couple various engineering phenomena, such as heat conduction and thermal fluids. The multiphysics DFEM-S system is solved using fixed-point iteration with a fully asynchronous parallel sweeper, unstructured coarse-mesh finite difference acceleration, and a multi-timescale improved quasi-static method scheme. The implementation is applied to a multiphysics microreactor model, with two transients: one initiated by a single heat-pipe failure and another by control drum rotation. Importantly, these examples demonstrate the ability of Griffin to tractably solve the neutron transport equation considering seven independent variables and feedback.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Active oxygen species in heterogeneously catalyzed oxidation reactions

Active oxygen species generation in heterogeneously catalyzed hydrocarbon oxidation reactions have not been fully characterized. There is little consensus as to which the most active or selective species are. However, electron localization at a single oxygen atom—rather than shared over several—is necessary to activate very stable hydrocarbons like methane. This paper presents an overview of oxygen species formed on various catalytic surfaces and their role in hydrocarbon oxidation reactions. Since limitations of analytical methods are a significant bottleneck for understanding and engineering active oxygen species, approaches for characterizing and quantifying active oxygen are discussed. While several methods are effective for describing certain oxygen species, there is no method that comprehensively characterizes all active oxygen species. Here, the most promising approach is oxygen isotope exchange coupled with infrared spectrometry and inline mass spectrometry.

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

Computational modeling of microalgal biofilm growth in heterogeneous rotating algal biofilm reactors (RABRs) for wastewater treatment

Rotating algal biofilm reactors (RABRs) are innovative systems designed to cultivate microalgae biofilms efficiently. In this paper, we have developed a novel mathematical model to accurately capture the growth dynamics of algae biofilms within RABR. By considering the spatial heterogeneity of the RABR, we introduce a PDE-based model that addresses the spatial variations across the substratum, enabling a more accurate simulation of biofilm growth in RABRs. The photosynthesis process is modeled through reactive kinetics, driving the growth of the algae biofilm. To analyze the system's behavior, we employ finite difference numerical methods to solve the complex PDE model. We then conduct extensive numerical simulations to understand algae biofilm growth in the RABR environment under various operational factors and environmental conditions. One primary focus in these simulations is to investigate the impact of various harvesting strategies, harvesting frequencies, light intensity, and light exposure on the overall biomass productivity of the algae biofilm. The numerical results provide valuable insights into optimizing algae biofilm growth and designing harvesting techniques in RABR systems. Our proposed novel mathematical model provides an effective platform for the theoretical investigation and design of RABRs for wastewater treatment.

09 BIOMASS FUELS↗
DOE OSTI2020

Probing Heterogeneity in Bovine Enamel Composition through Nanoscale Chemical Imaging using Atom Probe Tomography

Objective: The aim of this study was to determine the heterogeneity in chemical composition of bovine enamel using atom probe tomography, and thereby evaluate the suitability of bovine enamel as a substitute for human enamel in in vitro dental research. Design: Enamel samples from extracted bovine incisor teeth were first sectioned using a diamond saw and then milled into needle-like samples (<100 nm diameter) by focused ion beam (FIB) coupled with a scanning electron microscope (SEM). The samples were then analyzed in the atom probe to acquire three-dimensional (3D) images and quantify the atomic chemistry and distribution in bovine enamel. Results: For the first time, the atomic-level composition and clustering of major constituents and impurities within bovine enamel were determined and imaged. We discovered that the chemical composition of bovine enamel is spatially inhomogeneous at the atomic scale. The average bulk Ca/P ratio, ~1.4, was in agreement with previously reported literature values from alternative conventional methods. When assessed locally at the atomic scale, the Ca/P ratio varied between 1.1 and 2.03. We also discovered that the Mg impurities were significantly segregated throughout the enamel, and such clustering influenced the variation of Ca/P ratios. The increase in Mg concentrations, near the Mg clusters, correlated with increased Ca and decreased P concentrations. Conclusion: In conclusion, the presented findings of variability in local composition should be taken into account when interpreting dental research results from bovine enamel.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

A review of in situ/operando studies of heterogeneous catalytic hydrogenation of CO 2 to methanol

Repurposing CO 2 into chemicals, one of the Carbon Dioxide Removal (CDR) strategies, still faces significant challenges in conversion and energy efficiency due to the lack of effective catalysts and processes. Fundamental understanding through in situ/operando investigations of the reaction mechanisms and catalyst structures is pivotal for developing the efficient catalysts. This paper reviews the past and recent in situ/operando studies of methanol synthesis from heterogeneous CO 2 hydrogenation over a few typical catalysts including Cu-based, oxide-based, and noble-metal-based catalysts. With the development of high-pressure reactors, in situ/operando IR, X-ray absorption spectroscopy (XAS), X-ray diffraction (XRD), neutron diffraction and imaging have been used to reveal the surface intermediates and structures of working catalysts under CO 2 hydrogenation conditions. On the one hand, the combined operando techniques shed light on working mechanisms for some catalytic systems. On the other hand, due to the complexity of selective CO 2 hydrogenation reaction and limited sensitivity of current accessible operando techniques to the surface structure of catalysts, it is still murky about the exact nature of the active sites at the surface/interface and how different sites promote different reaction paths. Furthermore, it is concluded that new methodologies for differentiating signals from different sites, and the development of surface-sensitive techniques for high pressure reactions are needed for providing structural descriptors of highly active and selective CO 2 conversion catalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Microwave-assisted depolymerization of PET over heterogeneous catalysts

The accumulation of plastic waste in the environment is rising and poses a significant environmental and health threat. Polyethylene terephthalate (PET), used mainly in single-use plastic bottles, accounts for a substantial fraction of this waste. The currently employed recycling strategies are inadequate to deal with the global plastic waste problem. PET glycolysis is a promising depolymerization method but has mainly been conducted using homogeneous catalysts; developing active and sustainable catalysts, and more energy-efficient processes remain challenges. Here, in this work, we implement microwave heating in PET glycolysis and identify ZnO as an excellent heterogeneous catalyst. We demonstrate the influence of ZnO's particle size and facet and hydrogen bonding on its activity. Finally, we demonstrate excellent performance for real-world post-consumer PET waste deconstruction with rapid depolymerization into bis(2-hydroxyethyl) terephthalate (BHET), achieving > 95% yields in less than 10 min. Cradle-to-gate life cycle assessment also indicates that BHET produced by ZnO-catalyzed glycolysis has lower global warming potential than the petrochemical-based production and homogeneously-catalyzed glycolysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

A Green’s function fast multipole method for computation of micromechanical fields in heterogeneous materials

Computation of micromechanical fields in heterogeneous materials is usually performed using either the finite element method or the Green’s function method based on FFTs. The finite element method allows for accurate discretization and for non-periodic boundary conditions but is computationally expensive. On the other hand, the FFT-based method is computationally efficient but requires discretization on a regular grid of hexahedral voxels. In this paper, a Green’s function method allowing for accurate discretization using tetrahedral elements and for non-periodic boundary conditions is proposed. The convolution is computed using the fast multipole method, which provides good accuracy even for low-order expansion due to the fast decay of interactions between elements. The proposed Green’s function fast multipole method is verified by comparison with analytical and FFT-based solutions. Furthermore, the computational time is analyzed and compared to the FFT-based method for non-periodic convolution. Finally, effective properties of an elastic polycrystalline microstructure containing thin intergranular cracks are computed and analyzed.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A novel ignition model for low velocity impact of heterogeneous explosives based on interacting hot spots

While numerous studies have focused on the ignition of explosives occurring in high velocity impact and the associated shock-to-detonation transition, there has been growing interest in developing computational models focused on low-velocity impact regimes. A predictive low-velocity impact ignition model will be important for analyzing high explosive safety and potential accident scenarios. This work introduces a novel ignition model based on the concept of thermally interacting hot spots to simulate low velocity impacted heterogeneous explosives where observed ignition times are on the order of milliseconds. The model asserts that relevant hot spots are micron-sized, the typical separation between neighboring hot spots is on the order of a hundred microns, and that neighbors interact thermally through heat conduction across the interstitial region between them. To achieve tractable numerical solutions, hot spots are assumed to form a periodic array as opposed to the highly irregular positioning in an actual explosive. This idealization allows a single two hotspot system to characterize the ignition process. Consequently, the model is referred to as the two hot spot Frank-Kamenetskii ignition model. In the present study, hot spots are modeled as constant heat sources terms, but this can be extended to include grain-scale phenomena like frictional heating of micron-sized growing cracks that are confined under high pressure. Because the micron-sized features are below the scale that can be efficiently resolved at a systems level, an efficient subscale scheme based on the Method of Weighted Residuals (MWR) is used to efficiently solve the equations. In conclusion, we carry out numerical examples and analytic predictions illustrating the accuracy and the functioning of the model.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

From single cell to spatial multi-omics: unveiling molecular mechanisms in dynamic and heterogeneous systems

Single-cell multi-omics and spatial technology have been widely applied to biomedical studies and recently to environmental studies. Here, the cell size detected by single-cell omics ranges from ~2 µm (e.g., Bacillus subtilis) to ~120 µm (e.g., human oocytes). Simultaneous detection of single-cell multi-omics is available to human and plant tissues while limited to microbial samples. Spatial technology enables mapping the detected biomolecules in situ. The recent advances in MALDI-MSI and micro/nanoPOTS for the first time allow the application of spatial multi-omics in highly heterogeneous environmental samples composed of plants, fungi, and bacteria. We envision that these technologies will continue to advance our understanding of unique cell types, their developmental trajectory, and the intercellular signaling and interaction within biological samples.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Enabling end-to-end secure federated learning in biomedical research on heterogeneous computing environments with APPFLx

Facilitating large-scale, cross-institutional collaboration in biomedical machine learning (ML) projects requires a trustworthy and resilient federated learning (FL) environment to ensure that sensitive information such as protected health information is kept confidential. Specifically designed for this purpose, this work introduces APPFLx - a low-code, easy-to-use FL framework that enables easy setup, configuration, and running of FL experiments. APPFLx removes administrative boundaries of research organizations and healthcare systems while providing secure end-to-end communication, privacy-preserving functionality, and identity management. Furthermore, it is completely agnostic to the underlying computational infrastructure of participating clients, allowing an instantaneous deployment of this framework into existing computing infrastructures. Experimentally, the utility of APPFLx is demonstrated in two case studies: (1) predicting participant age from electrocardiogram (ECG) waveforms, and (2) detecting COVID-19 disease from chest radiographs. Here, ML models were securely trained across heterogeneous computing resources, including a combination of on-premise high-performance computing and cloud computing facilities. By securely unlocking data from multiple sources for training without directly sharing it, these FL models enhance generalizability and performance compared to centralized training models while ensuring data remains protected. In conclusion, APPFLx demonstrated itself as an easy-to-use framework for accelerating biomedical studies across organizations and healthcare systems on large datasets while maintaining the protection of private medical data.

Biomedical Research↗
DOE OSTI2021

Power allocation by load aggregator with heterogeneous loads using weighted projection

With the recently increasing adoption of transactive energy markets, the role of a load aggregator is becoming increasingly important. Among the many tasks of an aggregator, the task of managing the electricity consumption among a group of grid-interactive efficient buildings according to the demand on the grid is taken up. Specifically, here, the task of allocating a certain amount of power among the participating customers while respecting their preferences and comfort is considered. Modeling this as an aggregate flexibility, the virtual battery conditions that are employed to verify the feasibility of a candidate power profile are derived. A feasible power profile, when allocated, satisfies specified comfort bands. To determine the ideal power requirement of the loads, the model-free control method is employed. The feasible power profile is allocated by embedding the customers’ preferences through weights, posing the power constraint as a hyperplane, and employing a weighted projection from the ideal power requirements onto the constraint plane to solve the considered task. The proposed method is computationally efficient and scalable to any number of heterogeneous thermostatically controlled loads in buildings.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Advanced diagnostics to evaluate heterogeneity in lithium-ion battery modules

Battery packs for electric and stationary applications experience varied operating conditions, including abuse—e.g., fast charging, overcharging, thermal, vibration, shock, etc.—throughout their lifetimes. Innovative diagnostic tools and algorithms that go beyond single cells and deal with modules and packs are essential for early detection of off-normal issues. High-resolution tools with known detection limits are key to developing appropriate mitigation strategies. With the advent of rapid impedance spectroscopy that can generate a broadband impedance spectrum in ~10 sec, the case for impedance-based diagnostics that can be readily aligned with other methods, such as incremental capacity or dQ.dV -1 , has become promising. This study used the aforementioned diagnostic methods to identify realistic in-vehicle battery abnormalities (e.g., localized self-discharge and non-uniform aging), in series (up to 10S) and parallel (4P) strings, using 16 Ah graphite/NMC cells. The impedance-based diagnostic is found to be sensitive to the string size and state. Depending on the type of abnormality, detection frequency varied. The dQ.dV -1 method showed the potential to detect long-term aging-related heterogeneity in modules. In general, both the impedance and dQ.dV -1 methods were able to detect series strings’ abnormalities, but struggled to find those issues within parallel modules.

25 ENERGY STORAGE↗
DOE OSTI2021

A Physics-Constrained Deep Learning Model for Simulating Multiphase Flow in 3D Heterogeneous Porous Media

Physics-based simulators for multiphase flow in porous media emulate nonlinear processes with coupled physics, and usually require extensive computational resources for software development, maintenance and simulation execution. As a result, a huge demand exists for fast modeling of coupled processes in a wide range of subsurface applications including geological sequestration, hydrocarbon recovery and geothermal energy extraction. In this work, an efficient physics-constrained deep learning model is developed for solving multiphase flow in 3-Dimensional (3D) heterogeneous porous media. The model fully leverages the spatial topology predictive capability of convolutional neural networks, specifically U-Net with successive contracting and expansive steps, and is coupled with an efficient continuity-based smoother to predict flow responses that need spatial continuity. Furthermore, the transient regions are penalized to steer the training process such that the model can accurately capture flow in these regions. The model takes inputs including properties of porous media, fluid properties and well controls, and predicts the temporal-spatial evolution of the state variables (pressure and saturation). While maintaining the continuity of fluid flow, the 3D spatial domain is decomposed into 2D images for reducing training cost, and the decomposition results in an increased number of training data samples and better training efficiency. Additionally, a surrogate model is separately constructed as a postprocessor to calculate well flow rate based on the predictions of state variables from the deep learning model. We use the example of CO 2 injection into saline aquifers, and apply the physics-constrained deep learning model that is trained from physics-based simulation data and emulates the physics process. The model performs prediction with a speedup of ~ 1400 times compared to physics-based simulations, and the average temporal errors of predicted pressure and saturation plumes are 0.27% and 0.099% respectively. Furthermore, water production rate is efficiently predicted by a surrogate model for well flow rate, with a mean error less than 5%. Therefore, with its unique scheme to cope with the fidelity in fluid flow in porous media, the physics-constrained deep learning model can become an efficient predictive model for computationally demanding inverse problems or other coupled processes.

58 GEOSCIENCES↗
DOE OSTI2022

Sodium diffusion in heterogeneous porous media: Connecting laboratory experiments and simulations

Sodium has been suggested as a tracer for brine in reservoir formations where a significant amount of sodium ion is found mainly in the aqueous phase. Fortunately, the most abundant sodium isotope, 23 Na, is Nuclear Magnetic Resonance (NMR) active, making it possible to study the structure and dynamical properties of both bulk and pore confined fluid systems. We investigated the diffusion of key dissolved sodium species in bulk solution and porous media as a function of composition, ionic strength, and permeability of the carbonate samples using NMR relaxometry and pulsed-field gradient (PFG) NMR techniques. We use Indiana limestone as an example of natural porous media and water as a freely diffusible tracer and carrier. We demonstrate apparent diffusion measurement of sodium based on changes in spin–spin relaxation time (T 2 ) signal in pore confinement. The diffusion rate of sodium decreases with increasing counter-ion size. This effect is greater at higher ionic strengths and lower chemical potentials in porous media. The reactive transport code, CrunchFlow, was used to complement the NMR experiments to simulate diffusion behavior in porous media. Combining these two methods provides a powerful approach to estimating effective diffusion coefficients in heterogeneous matrices. The modeling considers the influence of physical properties (porosity and tortuosity) and chemical properties (geochemical composition and chemical gradients). The results presented in this work highlight the advantage of measuring apparent diffusivity using NMR T 2 relaxometry in conjunction with numerical simulation to derive effective diffusivity and the corresponding matrix properties (i.e., tortuosity) of the system.

58 GEOSCIENCES↗
DOE OSTI2020

Wellbore injectivity response to step-rate CO 2 injection: Coupled thermo-poro-elastic analysis in a vertically heterogeneous formation

Safe and permanent carbon geological storages require an elaborate analysis of geomechanical stability. Subsurface injection of CO 2 changes the local temperature and pore pressure and, further, alters the stress due to thermo-poro-elastic responses. A permanent CO 2 storage testing was conducted in the water leg of Cranfield reservoir in Mississippi, USA, where hosted CO 2 enhanced-oil recovery activities. During CO 2 injection, the injection rate was ramped up twice but the bottom-hole pressure did not increase with the imposed injection rates as expected. This unexpected field observation suggests the possibility of an open-mode fracture development at the injector. However, the injector response and potential fracture development have not been rigorously interpreted with detailed near-wellbore temperature and pore pressure change upon the CO 2 injection. In this study, we performed history matching of CO 2 injection using coupled thermo-poro-elastic reservoir simulation to examine the possibility of fracturing. We built a reservoir model including vertical heterogeneity in both petrophysical and geomechanical properties estimated from well-logging analysis and laboratory experiments. The simulation results show that CO 2 injection changes stresses and support the hypothesis of development of an open-mode fracture at the injector during the Cranfield test. The near-injector region exhibits a large temperature reduction up to 55 °C with ensuing effective horizontal stress reduction up to 9.1 MPa. However, a caprock integrity issue is unlikely because of the horizontal stress contrast within layers and low hydraulic communication with the injection zone. This work indicates that the injectant temperature should be considered in the design of high-rate CO 2 injector.

58 GEOSCIENCES↗
DOE OSTI2024

Heterogeneous oxidation of hydrogen-natural gas blends in a safe, clean, and efficient burner design

The growth of hydrogen as an alternative clean fuel for fulfilling thermal energy needs of multiple economic sectors globally requires access to reliable, safe, energy efficient, emission free combustion technology. The work described in this short communication shows the applicability of a novel heterogeneous combustion design in utilizing a wide range of hydrogen blended methane concentrations to cleanly and safely generate thermal energy. The utilization of a simple first principles design approach along with engineered materials yielded a fuel-flexible hybrid infrared-convection burner design capable of lowering NOx emissions by more than 95% while simultaneously improving the heat transfer efficiency by 15% with a wide range of hydrogen concentrations. Here, the safety and performance of the combustion design was demonstrated in an burner at capacities of up to 12,000 Btu/h in producing temperatures of 900 °C. The atmospheric burner concept was integrated in a cooktop configuration with combined thermal power rating of 30,000 Btu/h and operated with hydrogen blended natural gas.

08 HYDROGEN↗
DOE OSTI2022

Heterogenous activation of dynamic recrystallization and twinning during friction stir processing of a Cu-4Nb alloy

An interplay between high degree of shear deformation and deformation-induced heating occurs during friction stir processing (FSP) of metals. In medium-to-low stacking fault energy Cu alloys, this can lead to a complex spatially heterogenous activation of dynamic recrystallization (DRX) and twinning mechanisms. Within the Cu-Nb system, the presence of Nb is further expected to influence the DRX mechanism of the Cu matrix. However, the microstructural changes induced by the co-deformation of Nb during FSP are still not well understood. So, this study uses a combination of multimodal microstructural characterization, solution thermodynamics-based predictions, and computational crystal plasticity simulation to reveal the various microstructural evolution mechanisms that can occur during FSP of a Cu-4at.%Nb binary model alloy. The formation of softer DRX zones, and harder shear localization regions are revealed using electron backscatter diffraction, transmission electron microscopy, atom probe tomography, and crystal plasticity modeling.

36 MATERIALS SCIENCE↗