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At least 235 records · Page 13

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NASA NTRS2018

Scale-Resolving Simulations of Low-Pressure Turbine Cascades with Wall Roughness Using A Spectral-Element Method

The accurate prediction of wall-roughness effects in turbomachinery is becoming critical as turbine designers address airfoil surface quality and degradation concerns arising from the shift to advanced ceramic matrix composite (CMC) or additively-manufactured airfoils operating in higher temperature environments. In this paper, a recently developed computational capability for accurate and efficient scale-resolving simulations of turbomachinery is extended to analyze the boundary- layer separation and transition characteristics in a rough-wall low-pressure turbine (LPT) cascade. The computational capability is based on an entropy-stable discontinuous-Galerkin spectral-element approach that extends to arbitrarily high orders of spatial and temporal accuracy, and is implemented in an efficient manner for a modern high performance computer architecture. Results from the scale-resolving simulations of both smooth and rough airfoil cascades are presented and compared to previous experiments and numerical simulations. The results show that the suction surface boundary layer undergoes laminar separation, transition, and turbulent reattachment for the smooth airfoil cascade, while in the presence of roughness the separation and transition behavior of the suction surface boundary layer is substantially modified. The differences between the smooth and rough airfoil cascades are then highlighted by a detailed analysis of their respective turbulent flow fields.

Spectral-Element↗

NASA NTRS2021

Time Resolved Particle Image Velocimetry in Nozzle Flow

Temporally resolved particle image velocimetry (TR-PIV) is the newest and most exciting tool recently developed to support our continuing efforts to characterize and improve our understanding of the decay of turbulence in jet flows—a critical element for understanding the acoustic properties of the flow. A new TR-PIV system has been developed at the NASA Glenn Research Center which is capable of acquiring planar PIV image frame pairs at up to 25 kHz. The data reported here were collected at Mach numbers of 0.5 and 0.9 and at temperature ratios of 0.89 and 1.76. The field of view of the TR-PIV system covered six nozzle diameters along the lip line of the 50.8 mm diameter jet. The cold flow data at Mach 0.5 were compared with hotwire anemometry measurements in order to validate the new TR-PIV technique. The axial turbulence profiles measured across the shear layer using TR-PIV were thinner than those measured using hotwire anemometry and remained centred along the nozzle lip line. The collected TR-PIV data illustrate the differences in the single point statistical flow properties of cold and hot jet flows. The planar, time-resolved velocity records were then used to compute two-point space–time correlations of the flow at the Mach 0.9 flow condition. The TR-PIV results show that there are differences in the convective velocity and growth rate of the turbulent structures between cold and hot flows at the same Mach number.

Temporally Resolved Particle Image Velocimetry↗

DOE OSTI2020

Spatially Resolved Performance Analysis for Perovskite Solar Cells

This review explores the current state of the art in spatially resolved characterization of mixed-halide perovskite solar cells. As the size of perovskite cells and modules continues to grow, quantification of the spatial distribution of key cell parameters will become increasingly valuable in predicting ultimate cell-level performance and tracking process homogeneity. Here, both high resolution microscopic approaches using scanning techniques and camera-based methods for full-area cell and/or module analysis are discussed. The value of this local data in predicting performance losses at the cell level is particularly emphasized. Measurable physical parameters sensitive to losses of voltage, current, fill factor, and efficiency are discussed together with selected experimental results. It is demonstrated that a combination of spatially resolved cell parameter mapping/imaging can be used to quantitatively discriminate various loss contributions at high resolution. The impact and control of inhomogeneities become particularly important when upscaling from small devices to large formats compatible with industrial mass production.

14 SOLAR ENERGY↗

DOE OSTI2023

Resolving the Heat Generated from ZrO 2 Atomic Layer Deposition Surface Reactions

Abstract In situ pyroelectric calorimetry and spectroscopic ellipsometry were used to investigate surface reactions in atomic layer deposition (ALD) of zirconium oxide (ZrO 2 ). Calibrated and time‐resolved in situ ALD calorimetry provides new insights into the thermodynamics and kinetics of saturating surface reactions for tetrakis(dimethylamino)zirconium(IV) (TDMAZr) and water. The net ALD reaction heat ranged from 0.197 mJ cm −2 at 76 °C to 0.155 mJ cm −2 at 158 °C, corresponding to an average of 4.0 eV/Zr at all temperatures. A temperature dependence for reaction kinetics was not resolved over the range investigated. The temperature dependence of net reaction heat and distribution among metalorganic and oxygen source exposure is attributed to factors including growth rate, equilibrium surface hydroxylation, and the extent of the reaction. ZrO 2 ‐forming surface reactions were investigated computationally using DFT methods to better understand the influence of surface hydration on reaction thermodynamics.

Bielinski, Ashley R.↗

DOE OSTI2023

Resolving the Heat Generated from ZrO 2 Atomic Layer Deposition Surface Reactions

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

A Spatially Resolved Evaluation of Accelerated Environmental Aging on Emerging Polypropylene-Based Photovoltaic Backsheets Using Raman Spectroscopy

For this work, accelerated aging was used to assess environmental degradation in emerging co-extruded polypropylene (PP)-based backsheets under three different environmental conditions (65°C/20% relative humidity (RH), 75°C/20% RH, and 75°C/50% RH). Although differential scanning calorimetry did not measure crystallinity changes with exposure, spatially resolved Raman spectroscopy identified crystallinity increases in the core layer of aged samples, indicating a heterogeneous postcrystallization process. The Raman results were in agreement with synchrotron-based microfocused wide-angle X-ray scattering measurements. Cross-sectional nanoindentation was used to correlate localized crystallinity shifts with changes in Young's modulus. A similar trend was found where increased modulus was measured in the core layer, supporting the relationship between modulus and crystallinity. Finally, dielectric characterization was used to assess the impact of these material property changes on performance. While changes in the backsheet material properties and dielectric performance were observed with accelerated aging, these shifts generally equilibrated with time, indicating overall stability in response to environmental stressors. Additionally, the identified heterogeneous material property changes indicate that spatially resolved crystallinity measurements may be a valuable early failure indicator to be used in the assessment of PV backsheet long-term durability.

36 MATERIALS SCIENCE↗

DOE OSTI2021

In Operando Angle‐Resolved Photoemission Spectroscopy with Nanoscale Spatial Resolution: Spatial Mapping of the Electronic Structure of Twisted Bilayer Graphene

To pinpoint the electronic and structural mechanisms that affect intrinsic and extrinsic performance limits of 2D material devices, it is of critical importance to resolve the electronic properties on the mesoscopic length scale of such devices under operating conditions. Herein, angle‐resolved photoemission spectroscopy with nanoscale spatial resolution (nanoARPES) is used to map the quasiparticle electronic structure of a twisted bilayer graphene device. The dispersion and linewidth of the Dirac cones associated with top and bottom graphene layers are determined as a function of spatial position on the device under both static and operating conditions. The analysis reveals that microscopic rotational domains in the two graphene layers establish a range of twist angles from 9.8° to 12.7°. Application of current and electrostatic gating lead to strong electric fields with peak strengths of 0.75 V/μm at the rotational domain boundaries in the device. These proof‐of‐principle results demonstrate the potential of nanoARPES to link mesoscale structural variations with electronic states in operating device conditions and to disentangle such extrinsic factors from the intrinsic quasiparticle dispersion.

Majchrzak, Paulina↗

DOE OSTI2020

Toward particle-resolved accuracy in Euler–Lagrange simulations of multiphase flow using machine learning and pairwise interaction extended point-particle (PIEP) approximation

This study presents two different machine learning approaches for the modeling of hydrodynamic force on particles in a particle-laden multiphase flow. Results from particle-resolved direct numerical simulations (PR-DNS) of flow over a random array of stationary particles for eight combinations of particle Reynolds number (Re) and volume fraction (φ) are used in the development of the models. The first approach follows a two-step process. In the first flow prediction step, the perturbation flow due to a particle is obtained as an axisymmetric superposable wake using linear regression. In the second force prediction step, the force on a particle is evaluated in terms of the perturbation flow induced by all its neighbors using the generalized Faxén form of the force expression. In the second approach, the force data on all the particles from the PR-DNS simulations are used to develop an artificial neural network (ANN) model for direct prediction of force on a particle. Due to the unavoidable limitation on the number of fully resolved particles in the PR-DNS simulations, direct force prediction with the ANN model tends to over-fit the data and performs poorly in the prediction of test data. In contrast, due to the millions of grid points used in the PR-DNS simulations, accurate flow prediction is possible, which then allows accurate prediction of particle force. This hybridization of multiphase physics and machine learning is particularly important, since it blends the strength of each, and the resulting pairwise interaction extended point-particle model cannot be developed by either physics or machine learning alone.

42 ENGINEERING↗

DOE OSTI2023

Recurrent Convolutional Deep Neural Networks for Modeling Time-Resolved Wildfire Spread Behavior

The increasing incidence and severity of wildfires underscores the necessity of accurately predicting their behavior. While high-fidelity models derived from first principles offer physical accuracy, they are too computationally expensive for use in real-time fire response. Low-fidelity models sacrifice some physical accuracy and generalizability via the integration of empirical measurements, but enable real-time simulations for operational use in fire response. Machine learning techniques have demonstrated the ability to bridge these objectives by learning first-principles physics while achieving computational speedups. While deep learning approaches have demonstrated the ability to predict wildfire propagation over large time periods, time-resolved fire-spread predictions are needed for active fire management. Here, in this work, we evaluate the ability of deep learning approaches in accurately modeling the time-resolved dynamics of wildfires. We use an autoregressive process in which a convolutional recurrent deep learning model makes predictions that propagate a wildfire over 15 min increments. We apply the model to four simulated datasets of increasing complexity, containing both field fires with homogeneous fuel distribution as well as real-world topologies sampled from the California region of the United States. We show that even after 100 autoregressive predictions representing more than 24 h of simulated fire spread, the resulting models generate stable and realistic propagation dynamics, achieving a Jaccard score between 0.89 and 0.94 when predicting the resulting fire scar. The inference time of the deep learning models are examined and compared, and directions for future work are discussed.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Path-Integrated Stereo X-Ray Digital Image Correlation: Resolving the Violation of Conservation of Intensity

In this study, x-ray imaging addresses many challenges with visible light imaging in extreme environments, where optical diagnostics such as digital image correlation (DIC) and particle image velocimetry (PIV) suffer biases from index of refraction changes and/or cannot penetrate visibly occluded objects. However, conservation of intensity—the fundamental principle of optical image correlation algorithms—may be violated if ancillary components in the X-ray path besides the surface or fluid of interest move during the test. The test series treated in this work sought to characterize the safe use of fiber-epoxy composites in aerospace and aviation industries during fire accident scenarios. Stereo X-ray DIC was employed to measure test unit deformation in an extreme thermal environment involving a visibly occluded test unit, incident surface heating to temperatures above 600°C, and flames and soot from combusting epoxy decomposition gasses. The objective of the current work is to evaluate two solutions to resolve the violation of conservation of intensity that resulted from both the test unit and the thermal chamber deforming during the test. The first solution recovered conservation of intensity by pre-processing the path-integrated X-ray images to isolate the DIC pattern of the test unit from the thermal chamber components. These images were then correlated with standard, optical DIC software. The second solution, called Path-Integrated Digital Image Correlation (PI-DIC), modified the fundamental matching criterion of DIC to account for multiple, independently-moving components contributing to the final image intensity. The PI-DIC algorithm was extended from a 2D framework to a stereo framework and implemented in a custom DIC software. Both solutions accurately measured the cylindrical shape of the undeformed test unit, recovering radii values of R = 76.20±0.12 mm compared to the theoretical radius of R theor = 76.20 mm. During the test, the test unit bulged asymmetrically as decomposition gasses pressurized the interior and eventually burned in a localized jet. Both solutions measured the heterogeneous radius of this bulge, which reached a maximum of approximately R = 91 mm, with a discrepancy of 2–3% between the two solutions. Two solutions that resolve the violation of conservation of intensity for path-integrated X-ray images were developed. Both were successfully employed in a stereo X-ray DIC configuration to measure deformation of an aluminum-skinned, fiber-epoxy composite test unit in a fire accident scenario.

47 OTHER INSTRUMENTATION↗

DOE OSTI2021

Role of surface diffusion in formation of unique reactivity for graphite oxidation: Time-resolved measurements in a pulsed diffusion reactor

Quantification of oxidation kinetics is essential to develop graphitic materials for diverse applications: from refractories found in gas-cooled nuclear reactors to catalysts needed for chemical manufacturing. In this work, using well-defined highly oriented pyrolytic graphite, low-pressure isotopic transient experiments combined with controlled annealing periods, we resolve the role of surface diffusion and quantify oxidation kinetics with nanomole-precision. We observe an unexpected increase in reactivity following annealing which is explained by the role of surface diffusion increasing the probability for trapping mobile oxygen at more reactive edge sites. Here, the locus of adsorption and spillover to the basal plane is distinct from the trapping location creating a more active oxygen species. Isotopic products reflect the population dynamics of oxygen added at the edge and surface diffusion that relocates basal plane oxygen to more reactive edge sites. Since this process proceeds in parallel with direct oxidation reactions, it is not likely to be observed using steady-state or conventional ‘bulk’ characterization techniques. Our unique time-resolved non-equilibrium measurement in a well-defined transport regime, enables observation of three distinct behaviors: short-term deactivation due to the balance of rates in oxygen supply/product formation, reactivity increases due to surface diffusion and longer-term reactivity increase with oxygen accumulation.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Preliminary Studies of UV Photolysis of Gas-phase CH3I in Air: Time-resolved Infrared Identification of Methanol and Formaldehyde Products

Halogen-bearing compounds are known to photolyze under UV radiation; analysis of degradation products is of interest for both safety and environmental considerations. Time-resolved infrared spectroscopy was used in the laboratory to study the UV-photolysis of methyl iodide. Photolysis products methanol (CH3OH) and formaldehyde (HCHO) were clearly identified in a time-resolved manner. Preliminary kinetic models predict the formation of both CH3OH and HCHO.

Hughey, Kendall D.↗

DOE OSTI2024

A spatially-resolved model of neutron-irradiated tungsten coupling stochastic cluster dynamics and finite deformation plasticity

Structural materials used in nuclear reactors face severe degradation in mechanical properties, such as hardening and embrittlement. At the microscopic scale, this occurs due to creation and accumulation of irradiation-induced defects and their interaction with system dislocations. Although techniques exist which can model evolution of irradiation defects, for instance kinetic transport theory-based models, their interaction with mechanical deformation of the bulk material has not been investigated extensively. In this work, we demonstrate a novel spatially-resolved multiscale coupling between microscopic irradiation defect evolution, modeled using Stochastic Cluster Dynamics (SCD) and macroscopic mechanical deformation modeled using a finite-deformation plasticity model. SCD is used to determine the statistically averaged defect cluster spacing, dependent on operating conditions such as irradiation dose and temperature. This acts as an initial condition that governs the critical resolved shear stress of dislocation glide in the macroscopic plasticity model. This framework is used to predict mechanical behavior in post-mortem test of irradiated Tungsten samples, which has found its importance as structural material used in nuclear reactors. The results obtained using the coupled approach are in good agreement with experimental data of uniaxial tension tests. The model is able to capture the effect of temperature and irradiation dose on the material hardening. Two methods are proposed to estimate hardness – using Tabor's Law relating uniaxial yield stress to hardness and from flat-punch simulations. The results are in reasonable agreement with hardness data from micro-indentation experiments of irradiated Tungsten samples. Finally, the model is also able to reveal microstructural details such as spatial variation in defect density and local stress.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2026

Spatially resolved phase transformation mapping in 410 stainless steel during additive manufacturing visualized via real-time infrared data

Here, in this study, we demonstrate that spatially resolved cooling curves derived from real-time infrared (IR) thermography during additive manufacturing (AM) can capture spatial variations in phase transformation temperatures through cooling curve analysis (CCA). Using this approach, we show that during laser hot-wire deposition of 410 stainless steel (410SS), the martensite start temperature (M s ) evolves dynamically throughout the build. The M s temperature is spatially nonuniform, ranging from 185 °C to 348 °C, with the lowest values toward the build center and higher values toward the upper region of the deposit. In the lower portion of the build, no M s inflection is detected via CCA, consistent with transformation occurring earlier during thermal cycling followed by tempering during subsequent thermal cycles. These trends in M s are corroborated by characterizing the microstructure by electron backscatter diffraction (EBSD). Traditionally, M s is assumed to be constant, and a single interpass temperature is applied during both deposition and residual stress modeling. Our results demonstrate that IR-derived cooling curves provide a route to spatially and temporally resolved transformation temperature tracking for dynamic interpass control and improved residual-stress modeling.

Additive manufacturing↗

DOE OSTI2021

Feasibility of full-core pin resolved CFD simulations of small modular reactor with momentum sources

Complex flow structure interactions and heat transfer processes take place in nuclear reactor cores. Given the extreme pressure/temperature and radioactive conditions inside the core, numerical simulations offer an attractive and sometimes more feasible approach to study the related flow and heat transfer phenomena in addition to the experiments. Under the Exascale Computing Project, the full-core simulation of a small modular reactor (SMR) has been pursued coupling Computational Fluid Dynamics (CFD) and neutronics. A key aspect of the modeling of SMR fuel assemblies is the presence of spacer grids and the mixing promoted by mixing vanes or the equivalent. A reduced order methodology is adopted based on momentum sources to mimic the mixing of the vanes. The momentum sources have been carefully calibrated with detailed Large Eddy Simulations (LES) of spacer grids performed with Nek5000. Modeling the spacer grid and mixing vanes (SGMV) effect without body-fitted computational grid avoids the excessive costs in resolving the local geometric details, and thus supports the simulation to be scaled up to the full core. Besides the progress on momentum source modeling, this paper also features the first full-core pin resolved CFD simulation ever performed to the authors' knowledge. This represents a significant advancement in capability for the CFD of nuclear reactors, which will hopefully serve as an inspiration for further integrating high-fidelity numerical simulations in actual engineering designs.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

Multi-scale time-resolved electron diffraction: A case study in moiré materials

Ultrafast-optical-pump — structural-probe measurements, including ultrafast electron and x-ray scattering, provide direct experimental access to the fundamental timescales of atomic motion, and are thus foundational techniques for studying matter out of equilibrium. High-performance detectors are needed in scattering experiments to obtain maximum scientific value from every probe particle. Here we deploy a hybrid pixel array direct electron detector to perform ultrafast electron diffraction experiments on a WSe 2 /MoSe 2 2D heterobilayer, resolving the weak features of diffuse scattering and moiré superlattice structure without saturating the zero order peak. Enabled by the detector’s high frame rate, we show that a chopping technique provides diffraction difference images with signal-to-noise at the shot noise limit. Finally, we demonstrate that a fast detector frame rate coupled with a high repetition rate probe can provide continuous time resolution from femtoseconds to seconds, enabling us to perform a scanning ultrafast electron diffraction experiment that maps thermal transport in WSe 2 /MoSe 2 and resolves distinct diffusion mechanisms in space and time.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Simulating Droplet-Resolved Haze and Cloud Chemistry Forming Secondary Organic Aerosols in Turbulent Conditions within Laboratory and Cloud Parcels

Most of our existing knowledge of cloud chemistry in regards to forming secondary organic aerosols (SOA) is based on measurements in bulk aqueous solutions. However, SOA reaction kinetics derived from bulk solution measurements might differ from the kinetics in actual cloud droplets, since turbulent mixing and ionic strengths, and ratio of surface area to volume in individual cloud droplets might vary substantially from bulk solutions in the real atmosphere. Three-dimensional models at various scales have been used to simulate aqueous chemistry. However, most of these models do not resolve turbulence down to the smallest length scales of 1 mm and do not simulate cloud chemistry in individual cloud droplets due to large computational costs. Here we incorporate the formation of isoprene epoxydiol SOA (IEPOX-SOA) in individual droplets within a one-dimensional explicit mixing parcel model (EMPM-Chem). We apply EMPM-Chem to simulate turbulence and droplet-resolved IEPOX-SOA formation using a configuration based on the Michigan Tech Pi chamber. We find that the dissolution of IEPOX gases is weighted more towards larger cloud droplets due to their large liquid water content (compared to smaller droplets), while the conversion of dissolved IEPOX to IEPOX-SOA is much greater within smaller deliquesced haze particles due to their higher acidity and ionic strengths compared to cloud droplets. We also find that as droplet residence times increase in the chamber, e.g., due to increasing ammonium bisulfate seed aerosol injection rates and/or increasing heights of the chamber, formation of IEPOX-SOA increases substantially. Thus, our EMPM-Chem model could be used to design future cloud chambers to maximize SOA production from cloud chemistry. We also apply the EMPM-Chem model to simulate how IEPOX-SOA formation evolves in individual cloud droplets within rising cloudy parcels in the atmosphere. We find that as subsaturated air is entrained into and turbulently mixed with the cloud parcel, evaporation causes a reduction in droplet sizes, which leads to corresponding increases in per droplet ionic strength and acidity. Increased droplet acidity in turn greatly accelerates the kinetics of IEPOX-SOA formation. In conclusion, our results provide key insights into single-cloud-droplet chemistry, suggesting that entrainment mixing may be an important process that increases SOA formation in the real atmosphere.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Multioutput Convolutional Neural Network for Improved Parameter Extraction in Time-Resolved Electrostatic Force Microscopy Data

Time-resolved scanning probe microscopy methods, like time-resolved electrostatic force microscopy (trEFM), enable imaging of dynamic processes ranging from ion motion in batteries to electronic dynamics in microstructured thin film semiconductors for solar cells. Reconstructing the underlying physical dynamics from these techniques can be challenging due to the interplay of cantilever physics with the actual transient kinetics of interest in the resulting signal. Previously, quantitative trEFM used empirical calibration of the cantilever or feed-forward neural networks trained on simulated data to extract the physical dynamics of interest. Both these approaches are limited by interpreting the underlying signal as a single exponential function, which serves as an approximation but does not adequately reflect many realistic systems. Here, we present a multi-branched, multi-output convolutional neural network (CNN) that uses the trEFM signal in addition to the physical cantilever parameters as input. The trained CNN accurately extracts parameters describing both single-exponential and bi-exponential underlying functions, and more accurately reconstructs real experimental data in the presence of noise. This article demonstrates an application of physics-informed machine learning to complex signal processing tasks, enabling more efficient and accurate analysis of trEFM.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗