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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Assessing the use of portable air cleaners for reducing exposure to airborne diseases in a conference room with thermal stratification

The COVID-19 pandemic has highlighted the need for strategies that mitigate the risk of aerosol disease transmission in indoor environments with different ventilation strategies. It is necessary for building operators to be able to estimate and compare the relative impacts of different mitigation strategies to determine suitable strategies for a particular situation. Using a validated CFD model, this study simulates the dispersion of exhaled contaminants in a thermally stratified conference room with overhead heating. The impacts of portable air-cleaners (PACs) on the room airflow and contaminant distribution were evaluated for different PAC locations and flow rates, as well as for different room setups (socially distanced or fully occupied). To obtain a holistic view of a strategy's impacts under different release scenarios, we simultaneously model the steady-state distribution of aerosolized virus contaminants from eight distinct sources in 18 cases for a total of 144 release scenarios. The simulations show that the location of the source, the PAC settings, and the room set-up can impact the average exposure and PAC effectiveness. For this studied case, the PACs reduced the room average exposure by 31%-66% relative to the baseline case. Some occupant locations were shown to have a higher-than-average exposure, particularly those seated near the airflow outlet, and occupants closest to sources tended to see the highest exposure from said source. We found that these PACs were effective at reducing the stratification caused by overhead heating, and also identified at least one sub-optimal location for placing a PAC in this space.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Visual feedback and guided balance training in an immersive virtual reality environment for lower extremity rehabilitation

Balance training is essential for physical rehabilitation procedures, as it can improve functional mobility and enhance cognitive coordination. However, conventional balance training methods may have limitations in terms of motivation, real-time objective feedback, and personalization, which a virtual reality (VR) setup may provide a better alternative. In this work, we present an immersive VR training environment for lower extremity balance rehabilitation with real-time guidance and feedback. The VR training environment immerses the user in a 3D ice rink model where a virtual coach (agent) leads them through a series of balance poses, and the user controls a trainee avatar with their own movements. Here we developed two coaching styles: positive-reinforcement and autonomous-supportive, and two viewpoints of the trainee avatar: first-person and third-person. The proposed environment was evaluated in a user study with healthy, non-clinical participants (n = 16, 24.4 ± 5.7 years old, 9 females). Our results show that participants showed stronger performance in the positive-reinforcement style compared to the autonomous-supportive style. Additionally, in the third-person viewpoint, the participants exhibited more stability in the positive-reinforcement style compared to the autonomous-supportive style. For viewpoint, participants exhibited stronger performance in the first-person viewpoint compared to third-person in the autonomous-supportive style, while they were comparable in the positive-reinforcement style. We observed no significant effects on the foot height and number of mistakes. Furthermore, we report the analysis of user performance with balance training poses and subjective measures based on questionnaires to assess the user experience, usability, and task load. The proposed VR balance training could offer an interactive, adaptive, and engaging environment and open new potential research directions for lower extremity rehabilitation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Generalized moving least squares vs. radial basis function finite difference methods for approximating surface derivatives

Approximating differential operators defined on two-dimensional surfaces is an important problem that arises in many areas of science and engineering. Over the past ten years, localized meshfree methods based on generalized moving least squares (GMLS) and radial basis function finite differences (RBF-FD) have been shown to be effective for this task as they can give high orders of accuracy at low computational cost, and they can be applied to surfaces defined only by point clouds. However, there have yet to be any studies that perform a direct comparison of these methods for approximating surface differential operators (SDOs). The first purpose of this work is to fill that gap. For this comparison, we focus on an RBF-FD method based on polyharmonic spline kernels and polynomials (PHS+Poly) since they are most closely related to the GMLS method. Additionally, we use a relatively new technique for approximating SDOs with RBF-FD called the tangent plane method since it is simpler than previous techniques and natural to use with PHS+Poly RBF-FD. Further, the second purpose of this work is to relate the tangent plane formulation of SDOs to the local coordinate formulation used in GMLS and to show that they are equivalent when the tangent space to the surface is known exactly. The final purpose is to use ideas from the GMLS SDO formulation to derive a new RBF-FD method for approximating the tangent space for a point cloud surface when it is unknown. For the numerical comparisons of the methods, we examine their convergence rates for approximating the surface gradient, divergence, and Laplacian as the point clouds are refined for various parameter choices. We also compare their efficiency in terms of accuracy per computational cost, both when including and excluding setup costs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Silver-carbon interlayers in anode-free solid-state lithium metal batteries: Current development, interfacial issues, and instability challenges

As an interlayer between the anode and the electrolyte of the all-solid-state lithium metal batteries (ASSLMBs), the silver-carbon (Ag-C) nanocomposite has been reported to significantly increase the energy density and cycle rate of solid-state lithium metal batteries. Ag-C interlayers serve as mixed ionic-electronic conductor that conducts both Li+ ions and electrons and lithium storage capacity. Unfortunately, it was unclear how the Ag-C interlayer regulated lithium plating and stripping. Moreover, the structural and chemical instabilities between the interlayer and the electrolyte, within the interlayer, or beneath the interlayer on lithium substrate are likely to cause cell failure. In this review, we discuss interfacial issues and summarize recent progress in solution strategies for ASSLMBs, with a specific focus on the use of a silver-carbon (Ag-C) nanocomposite interlayer in anode-free setups. Based on the Li transport kinetics among the Ag-C interlayers, the interfacial configurations of Ag-C interlayers are classified as either exterior or internal. Furthermore, the review concludes with a discussion of the perspectives and future prospects, allowing for the improvement of interlayer techniques for solid-state batteries.

25 ENERGY STORAGE↗
DOE OSTI2023

A systematic multicriteria-based approach to support product portfolio selection in microalgae biorefineries

Here this work proposes and applies a sequential approach of objective methods to aid the decision-making process for the deployment of microalgae biorefineries. The strategy combines Multicriteria Decision Analysis (MCDA) and weight assignment methods to simultaneously consider technical, economic, and environmental criteria to (1) outrank the best bioproduct options from different biomass fractions present in microalgae biomass at different ratios (namely carbohydrates, lipids, and protein) and (2) define the most suitable biorefining pathways associated with specific pairings of microalgae strains and cultivation conditions. The first part of the assessment identified succinic acid, acrylic acid, and citric acid as the top-ranked bioproducts from carbohydrates, polyurethane from lipids, and thermoplastic extrusion co-feed from protein. The second step of the analysis determined that, when production of a hydrocarbon fuel is desired, the compositional profile of a strain is paramount in defining the biorefining setup that should be pursued. In summary, microalgae lipids should be sent to the production of hydrocarbon fuels if the ratio between neutral lipids and fermentable carbohydrates is higher than roughly 1, with carbohydrates and protein being converted to the higher-value products noted above. Finally, this result was corroborated through process simulations, which indicated superior economic and environmental metrics when strains are paired with suitable conversion pathways identified through MCDA based on their compositional profiles. The outcomes of this work provide clear, objective, guidelines for establishing the best biorefining approach for a large suite of biochemical compositions as a screening method prior to employing detailed process simulations alongside rigorous techno-economic and life-cycle assessments.

09 BIOMASS FUELS↗
DOE OSTI2024

NO formation by N 2 /O 2 plasma catalysis: The impact of surface reactions, gas-phase reactions, and mass transport

Pathways and timescales relevant to facilitate plasma-assisted N 2 -O 2 reactions are assessed by measuring the consumption of plasma-derived N and the formation of NO in the gas phase and over Ag catalytic surfaces. These measurements are enabled by a setup that enables N 2 activation in an atmospheric pressure RF plasma jet, enables O 2 addition in the plasma afterglow, facilitates reactions over an Ag wire catalyst, and allows species density quantification by molecular beam mass spectrometry. Gas-phase reactions consume N but do not form NO with high selectivity. The presence of the non-porous Ag wire catalyst increases the rate of N conversion to NO, though mass transfer processes, not surface reactions, dictate the rate of N consumption. When O 2 concentrations and the ratio of the surface area of the catalyst to the void volume of the reactor are high (3–5 mol% O 2 , 10900 m –1 ), N conversion to NO reaches 100 % selectivity. When both N 2 and O 2 are fed through the plasma jet, gas-phase NO production increases 10×, although plasma and gas-phase processes do not exclusively produce NO. Above a threshold NO density, N cannot diffuse to the catalyst surface faster than it is consumed in the gas phase by reactions with NO. Furthermore, the use of heterogeneous catalysts to enhance plasma-driven N x O y formation and control N x O y product selectivity is limited to cases where diffusive transport of N from the gas phase to the catalyst surface is faster than consumption of N from gas-phase reactions with NO.

Engineering↗
DOE OSTI2025

Evaluation of IrO 2 catalysts doped with Ti and Nb at industrially relevant electrolyzer conditions: A comprehensive study

A series of commercial Oxygen Evolution Reaction (OER) IrO 2 -based materials doped with acid-stable titanium and niobium species were comprehensively characterized by Brunauer-Emmett-Teller (BET), X-ray diffraction analysis (XRD), X-ray photoelectron spectroscopy (XPS), transmission electron microscopy (TEM) with energy dispersive spectroscopy (EDS), and X-ray Scattering. Electrocatalysts were integrated into Membrane Electrode Assembly (MEA) using a fabrication method developed under the US DOE H2NEW consortium. An electrolysis performance in a commercial setup as well as a laboratory screening system was performed at conditions relevant to industrial application. According to the comprehensive characterizations, the studied materials are closer to doped iridium oxides rather than core–shell structures. In an electrolysis cell, the IrO 2 /TiO x catalyst slightly outperforms the IrO 2 /NbO x based on the activity. It was demonstrated that the operation of electrolysis cells at elevated temperatures and the implementation of thinner Nafion-type membranes allows for substantially increased performance, which is consistent with the literature report. Finally, this work provides valuable baselines including characterization and performance for guiding future research in this direction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Capillary flow in UHPFRC with synthetic fibers, under high tensile stresses

A novel Ultra High-Performance Fiber Reinforced Concrete (UHPFRC) mix with synthetic fibers and a low clinker matrix, henceforth referred to as PE-UHPFRC, has been developed for structural applications. It exhibits a high tensile elastic limit above 7 MPa, a tensile strength of more than 10 MPa, and a very high tensile hardening domain of above 3.5%. In order to effectively use this material, its protective properties have been investigated on the basis of the effect of tensile deformation and subsequent cracking on water transport properties, for a wide range of tensile strain. A special setup was developed to measure the capillary absorption of liquids while the specimen is under tension. The results show a considerable reduction in capillary absorption in case of PE-UHPFRC compared with Strain Hardening Cement-based Composites (SHCC). Moreover, the results highlight the considerable effect of the onset of cracking on capillary absorption.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Predicting oxidation damage of ultra high-temperature carbide ceramics in extreme environments using machine learning

Determining the oxidation resistance of UHTC carbides in extreme environments is challenging theoretically and experimentally due to the high dimensional complexity of influencing variables and intricate testing setups. Herein we demonstrate the use of machine learning (ML) models trained with experimental literature data to predict the oxide thickness of UHTC carbides exposed to air based on composition, mean grain size, relative densification, holding time, and temperature. A multi-dimensional database with 76 occurrences is created containing experimental results of Hf, Zr, and Ta carbides plus additives. In this study, the preprocessed database is then used to train ML models to predict their oxidation behavior. The trained model predicts the oxidation damage in the form of an average oxide thickness in UHTC carbides with a Mean Absolute Error (MAE) of ±65.45 μm for samples in the testing set that developed thicknesses up to 1000 μm. The model successfully predicted oxidation damage for a recession rate lower than 60 μm/min. It is noticed that the ensemble method MAE is increased to ±134.34 μm while forecasting the oxidation of samples with a recession rate higher than the threshold. The unprecedented approach is a novel way to predict the damage through the oxidation of carbide compounds before processing for a smarter design with room for improvement.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Dynamic and static corrosion of chromium-alumina refractory in simulated nuclear waste glass

Monofrax® K-3, a chromium–alumina refractory, is widely used in nuclear waste glass melters due to its high durability. However, aggressive vitrification conditions still lead to corrosion that limits melter lifespan. Here, this study investigates two key corrosion modes, subsurface and melt-line corrosion, under static and dynamic conditions, using a custom setup enabling precise control of melt velocity and temperature. Experimental results, interpreted using diffusion-limited dissolution models, show that (i) subsurface corrosion increases by approximately 10% per mm/s increase in melt velocity, while melt-line corrosion remains virtually unaffected, (ii) corrosion rate dependence on temperature is inversely proportional to melt viscosity, and (iii) glass composition affects both corrosion modes similarly, based on Cr 2 O 3 , Al 2 O 3 , and alkali contents. The experimental data also allowed the determination of diffusion coefficients of major dissolving species, which were found to be consistent between the melt-line and subsurface corrosion models, indicating their suitability for future CFD studies.

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

Quantifying particle movement in a spout-fluidized bed with irregular feedstock morphology

Here, the spout-bed fluidization behavior of nonspherical, 140 μm SiC feedstock was quantified via particle image velocimetry for varying gas distributor geometries. A bench-scale, room-temperature fluidization setup was assembled to model a 50 mm fluidized bed chemical vapor deposition (FB-CVD) system, and fluidized bed motion was captured using a high-speed camera. Modular tips with varying inlet geometries were 3D printed and tested on the bench-scale rig using identical feedstock and gas flow rates in the range of 3.0–9.0 L/min. Fluidization behavior was quantified by extracting parameters of the bed velocity, frequency, dead time, and other measurements, which were ranked for each inlet geometry configuration tested. The results from this work demonstrate that changing the path of inlet gas flow can significantly change the hydrodynamics within a spout-fluidized bed under identical feedstock, loading, and flow rate conditions, potentially enabling experimental control of particle fluidization behavior for a given condition. Moreover, composite rankings of fluidization behavior for unique distributor geometries hold potential to guide the design of FB-CVD experiments for various engineering and scientific applications.

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

Interaction between dissolution and precipitation during olivine carbonation: Implications for CO 2 mineralization

Large-scale carbonation of olivine is considered a promising approach for in situ mineral carbonation, offering a permanent and stable method for CO 2 storage. A critical aspect of this process is understanding how dissolution and precipitation interact, as this could drive fracturing and enhance further reactions. In this study, we conducted carbonation experiments on olivine using CO 2 -saturated aqueous solutions of NaHCO 3 and NaCl. Two experimental setups were used: one representing an open geochemical system and the other a closed system, corresponding to reaction-limited and flow-limited scenarios, respectively. Further, post-reaction textural analysis using scanning electron microscopy (SEM) revealed surface coatings of reaction products in the closed system, while etch pits and etch channels were prevalent in the open system. Although no direct evidence of reaction-driven fracturing was observed, etch pits and etch channels may serve as initiation points for subcritical crack formation and growth, potentially maintaining permeability and exposing new unreacted surfaces. Using linear elastic fracture mechanics (LEFM) model, we estimate that microcracks could propagate under a pressure of 0.1 GPa if reaction products accumulate within the etch pits. Our findings offer new insights into the mechanisms governing olivine carbonation.

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

Melt pool instability detection using coaxial photodiode system validated by in-situ X-ray imaging

A co-axial photodiode monitoring system with high temporal resolution has been integrated into a proven test bench enabling synchronized side-view high speed X-ray imaging of melt pool dynamics and top-view spectral emission characterization of the melt pool. Here, this setup enables direct observation of melt pool phenomena and correlation between the two monitoring systems which can be directly scaled to commercial systems. The work demonstrates a 92% detection rate in keyhole collapse phenomena related to defect generation in SLM. Furthermore, the impact of gas flow on monitoring signals is studied to understand the fundamental importance of gas flow in commercial systems.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A comprehensive and fair comparison of two neural operators (with practical extensions) based on $\mathrm{FAIR}$ data

Neural operators can learn nonlinear mappings between function spaces and offer a new simulation paradigm for real-time prediction of complex dynamics for realistic diverse applications as well as for system identification in science and engineering. Herein, we investigate the performance of two neural operators, which have shown promising results so far, and we develop new practical extensions that will make them more accurate and robust and importantly more suitable for industrial-complexity applications. The first neural operator, DeepONet, was published in 2019 (Lu et al., 2019), and its original architecture was based on the universal approximation theorem of Chen & Chen (1995). The second one, named Fourier Neural Operator or FNO, was published in 2020, and it is based on parameterizing the integral kernel in the Fourier space. DeepONet is represented by a summation of products of neural networks (NNs), corresponding to the branch NN for the input function and the trunk NN for the output function; both NNs are general architectures, e.g., the branch NN can be replaced with a CNN or a ResNet. According to Kovachki et al. (2021), FNO in its continuous form can be viewed conceptually as a DeepONet with a specific architecture of the branch NN and a trunk NN represented by a trigonometric basis. In order to compare FNO with DeepONet computationally for realistic setups, we develop several extensions of FNO that can deal with complex geometric domains as well as mappings where the input and output function spaces are of different dimensions. We also develop an extended DeepONet with special features that provide inductive bias and accelerate training, and we present a faster implementation of DeepONet with cost comparable to the computational cost of FNO, which is based on the Fast Fourier Transform. Here we consider 16 different benchmarks to demonstrate the relative performance of the two neural operators, including instability wave analysis in hypersonic boundary layers, prediction of the vorticity field of a flapping airfoil, porous media simulations in complex-geometry domains, etc. We follow the guiding principles of FAIR (Findability, Accessibility, Interoperability, and Reusability) for scientific data management and stewardship. The performance of DeepONet and FNO is comparable for relatively simple settings, but for complex geometries the performance of FNO deteriorates greatly. We also compare theoretically the two neural operators and obtain similar error estimates for DeepONet and FNO under the same regularity assumptions.

42 ENGINEERING↗
DOE OSTI2022

Geometric prior of multi-resolution yielding manifolds and the local closest point projection for nearly non-smooth plasticity

Elastoplasticity models often introduce a scalar-valued yield function to implicitly represent the boundary between elastic and plastic material states. Here this paper introduces a new alternative where the yield envelope is represented by a manifold of which the topology and the geometry are learned from a set of data points in a parametric space (e.g. principal stress space, π-plane). Here, deep geometric learning enables us to reconstruct a highly complex yield envelope by breaking it down into multiple coordinate charts. The global atlas that consists of these coordinate charts in return allows us to represent the yield surface via multiple overlapping patches, each with a specific local parametrization. This setup provides several advantages over the classical implicit function representation approach. For instance, the availability of coordinate charts enables us to introduce an alternative stress integration algorithm where the trial stress may project directly on a local patch and hence circumvent the issues related to non-smoothness and the lack of convexity of yield surfaces. Meanwhile, the local parametric approach also enables us to predict hardening/softening locally in the parametric space, even without complete knowledge of the yield surface. Comparisons between the classical yield function approach on the non-smooth plasticity and anisotropic cam-clay plasticity model are provided to demonstrate the capacity of the models for highly precise yield surface and the feasibility of the implementation of the learned model in the local stress integration algorithm.

42 ENGINEERING↗
DOE OSTI2022

A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks

Physics-informed neural networks (PINNs) have shown to be effective tools for solving both forward and inverse problems of partial differential equations (PDEs). PINNs embed the PDEs into the loss of the neural network using automatic differentiation, and this PDE loss is evaluated at a set of scattered spatio-temporal points (called residual points). The location and distribution of these residual points are highly important to the performance of PINNs. However, in the existing studies on PINNs, only a few simple residual point sampling methods have mainly been used. Here, we present a comprehensive study of two categories of sampling for PINNs: non-adaptive uniform sampling and adaptive nonuniform sampling. We consider six uniform sampling methods, including (1) equispaced uniform grid, (2) uniformly random sampling, (3) Latin hypercube sampling, (4) Halton sequence, (5) Hammersley sequence, and (6) Sobol sequence. We also consider a resampling strategy for uniform sampling. To improve the sampling efficiency and the accuracy of PINNs, we propose two new residual-based adaptive sampling methods: residual-based adaptive distribution (RAD) and residual-based adaptive refinement with distribution (RAR-D), which dynamically improve the distribution of residual points based on the PDE residuals during training. Hence, we have considered a total of 10 different sampling methods, including six non-adaptive uniform sampling, uniform sampling with resampling, two proposed adaptive sampling, and an existing adaptive sampling. We extensively tested the performance of these sampling methods for four forward problems and two inverse problems in many setups. Our numerical results presented in this study are summarized from more than 6000 simulations of PINNs. Here, we show that the proposed adaptive sampling methods of RAD and RAR-D significantly improve the accuracy of PINNs with fewer residual points for both forward and inverse problems. Furthermore, the results obtained in this study can also be used as a practical guideline in choosing sampling methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Multi-output multilevel best linear unbiased estimators via semidefinite programming

Multifidelity forward uncertainty quantification (UQ) problems often involve multiple quantities of interest and heterogeneous models (e.g., different grids, equations, dimensions, physics, surrogate and reduced-order models). While computational efficiency is key in this context, multi-output strategies in multilevel/multifidelity methods are either sub-optimal or non-existent. In this paper we extend multilevel best linear unbiased estimators (MLBLUE) to multi-output forward UQ problems and we present new semidefinite programming formulations for their optimal setup. Not only do these formulations yield the optimal number of samples required, but also the optimal selection of low-fidelity models to use. While existing MLBLUE approaches are single-output only and require a non-trivial nonlinear optimization procedure, the new multi-output formulations can be solved reliably and efficiently. Here, we demonstrate the efficacy of the new methods and formulations in practical UQ problems with model heterogeneity.

97 MATHEMATICS AND COMPUTING↗