SEARCH · Search NASA

Results for “higher accuracy”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 415 records · Page 23

All sources NASA NTRS DOE OSTI

DOE OSTI2024

Projecting Large Fires in the Western US With an Interpretable and Accurate Hybrid Machine Learning Method

More frequent and widespread large fires are occurring in the western United States (US), yet reliable methods for predicting these fires, particularly with extended lead times and a high spatial resolution, remain challenging. In this study, we proposed an interpretable and accurate hybrid machine learning (ML) model, that explicitly represented the controls of fuel flammability, fuel availability, and human suppression effects on fires. The model demonstrated notable accuracy with a F 1 -score of 0.846 ± 0.012, surpassing process-driven fire danger indices and four commonly used ML models by up to 40% and 9%, respectively. More importantly, the ML model showed remarkably higher interpretability relative to other ML models. Specifically, by demystifying the “black box” of each ML model using the explainable AI techniques, we identified substantial structural differences across ML fire models, even among those with similar accuracy. The relationships between fires and their drivers, identified by our model, were aligned closer with established fire physical principles. The ML structural discrepancy led to diverse fire predictions and our model predictions exhibited greater consistency with actual fire occurrence. With the highly interpretable and accurate model, we revealed the strong compound effects from multiple climate variables related to evaporative demand, energy release component, temperature, and wind speed, on the dynamics of large fires and megafires in the western US. Our findings highlight the importance of assessing the structural integrity of models in addition to their accuracy. They also underscore the critical need to address the rise in compound climate extremes linked to large wildfires.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Epistatic Net allows the sparse spectral regularization of deep neural networks for inferring fitness functions

Abstract Despite recent advances in high-throughput combinatorial mutagenesis assays, the number of labeled sequences available to predict molecular functions has remained small for the vastness of the sequence space combined with the ruggedness of many fitness functions. While deep neural networks (DNNs) can capture high-order epistatic interactions among the mutational sites, they tend to overfit to the small number of labeled sequences available for training. Here, we developed Epistatic Net (EN), a method for spectral regularization of DNNs that exploits evidence that epistatic interactions in many fitness functions are sparse. We built a scalable extension of EN, usable for larger sequences, which enables spectral regularization using fast sparse recovery algorithms informed by coding theory. Results on several biological landscapes show that EN consistently improves the prediction accuracy of DNNs and enables them to outperform competing models which assume other priors. EN estimates the higher-order epistatic interactions of DNNs trained on massive sequence spaces-a computational problem that otherwise takes years to solve.

Aghazadeh, Amirali (ORCID:0000000302230873)↗

DOE OSTI2026

Testing of High S Matrix Glasses to Expand DFHLW Glass Compositional Ranges (Rev.1)

Gaps in glass composition-property data for direct-feed high-level waste (DFHLW) have recently been identified. One such gap is the region of high sulfur solubility since previous, pretreated, high-level wastes contained very little sulfur. Filling this data gap will significantly broaden the range of process flowsheet options including minimal washing and will allow for optimized waste loading in DFHLW glasses. This report summarizes the data collected during the characterization of the DFHLW High S Glass Matrix (HS24). A glass matrix of 50 glass compositions was developed to evenly cover the DFHLW composition region for high sulfur glass. Matrix glasses were designed to expand the composition region outside the current component concentration and property limits so as to reduce uncertainties at the limits. The 50 matrix glasses were fabricated and tested for properties important to the success of DFHLW vitrification including: compositions, canister centerline cooling (CCC) crystallinity and isothermal crystallinity, density, viscosity, electrical conductivity (EC), product consistency test (PCT) response, toxicity, and sulfate solubility. Melter materials corrosion testing is reported elsewhere. These glasses were intentionally designed to have high SO 3 solubilities (0.7 to 2.2 SO 3 wt%) in compositional regions that had not been previously explored. Forty-eight glasses showed the measured SO 3 content retained >80% of the target SO 3 and the densities of all the glasses ranged from 2.49 g·cm -3 to 2.74 g·cm -3 . While the model predicted nepheline formation in 5 glasses, one of the tested 50 CCC glasses formed nepheline, and 35 glasses formed Cr-containing phases such as spinels and eskolaite. Only five glasses were amorphous after CCC treatment where 44 glasses with detectable crystals contained =10 wt% crystals and only one glass had > 10 wt% crystals. None of the glasses exceeded the allowable T 2% for spinel crystal formation at 950 ºC (i.e., no glasses had >2 wt% spinel at 950 ºC) during isothermal crystal fraction tests where 10 glasses showed no crystalline phases at or below 950 ºC. All the glasses (except one which failed being slightly lower than the target) satisfied the SO 3 constraint while 98 glasses did not meet the viscosity constraints and 4 failed the EC constraints. Six quenched (Q) and six CCC glasses failed the Defense Waste Processing Facility (DWPF) Environmental Assessment (EA) glass PCT threshold and 3 Q and 4 CCC failed the PCT design constraint. One glass exceeded the WTP delisting limits for Cr via EPA Method 1311 (i.e., Toxicity Characteristic Leaching Procedure, TCLP). It should be emphasized that some of these glasses were specifically designed to approach or even exceed certain property constraints, as filling data gaps in these regions will provide the greatest benefit for future model development by improving accuracy and reducing uncertainties. These insights will ultimately support the development of more robust glass formulation strategies, enabling higher waste loading, reducing operational risks, and expanding the processing envelope.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2021

Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment

Here we present the results for CAPRI Round 50, the fourth joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of twelve targets, including six dimers, three trimers, and three higher-order oligomers. Four of these were easy targets, for which good structural templates were available either for the full assembly, or for the main interfaces (of the higher-order oligomers). Eight were difficult targets for which only distantly related templates were found for the individual subunits. Twenty-five CAPRI groups including eight automatic servers submitted ~1250 models per target. Twenty groups including six servers participated in the CAPRI scoring challenge submitted ~190 models per target. The accuracy of the predicted models was evaluated using the classical CAPRI criteria. The prediction performance was measured by a weighted scoring scheme that takes into account the number of models of acceptable quality or higher submitted by each group as part of their five top-ranking models. Compared to the previous CASP-CAPRI challenge, top performing groups submitted such models for a larger fraction (70–75%) of the targets in this Round, but fewer of these models were of high accuracy. Scorer groups achieved stronger performance with more groups submitting correct models for 70–80% of the targets or achieving high accuracy predictions. Servers performed less well in general, except for the MDOCKPP and LZERD servers, who performed on par with human groups. In addition to these results, major advances in methodology are discussed, providing an informative overview of where the prediction of protein assemblies currently stands.

protein complexes↗

DOE OSTI2024

Infrared Fingerprint and Unimolecular Decay Dynamics of the Hydroperoxyalkyl Intermediate (•QOOH) in Cyclopentane Oxidation

A transient carbon-centered hydroperoxyalkyl intermediate (•QOOH) in the oxidation of cyclopentane is identified by IR action spectroscopy with time-resolved unimolecular decay to hydroxyl (OH) radical products that are detected by UV laser-induced fluorescence. Two nearly degenerate •QOOH isomers, β- and γ-QOOH, are generated by H atom abstraction of the cyclopentyl hydroperoxide precursor. Fundamental and first overtone OH stretch transitions and combination bands of •QOOH are observed and compared with anharmonic frequencies computed by second-order vibrational perturbation theory. An OH stretch transition is also observed for a conformer arising from torsion about a low-energy CCOO barrier. Definitive identification of the β-QOOH isomer relies on its significantly lower transition state (TS) barrier to OH products, which results in rapid unimolecular decay and near unity branching to OH products. A benchmarking approach is utilized to compute high-accuracy stationary point energies, most importantly TS barriers, for cyclopentane oxidation (C 5 H 9 O 2 ), building on higher level reference calculations for ethane oxidation (C 2 H 5 O 2 ). The experimental OH product appearance rates are compared with computed statistical microcanonical rates using RRKM theory, including heavy-atom tunneling, thereby validating the computed TS barrier. The results are extended to thermal unimolecular decay rate constants at temperatures and pressures relevant to cyclopentane combustion via master-equation modeling. Furthermore, the various torsional and ring puckering states of the wells and transition states are explicitly considered in these calculations.

Chemical calculations↗

DOE OSTI2022

Global Sensitivity Analysis Using the Ultra‐Low Resolution Energy Exascale Earth System Model

Abstract For decades, Arctic temperatures have increased twice as fast as average global temperatures. As a first step toward quantifying parametric uncertainty in Arctic climate, we performed a variance‐based global sensitivity analysis (GSA) using a fully coupled, ultra‐low resolution (ULR) configuration of version 1 of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SMv1). Specifically, we quantified the sensitivity of six quantities of interests (QOIs), which characterize changes in Arctic climate over a 75 year period, to uncertainties in nine model parameters spanning the sea ice, atmosphere, and ocean components of E3SMv1. Sensitivity indices for each QOI were computed with a Gaussian process emulator using 139 random realizations of the random parameters and fixed preindustrial forcing. Uncertainties in the atmospheric parameters in the Cloud Layers Unified by Binormals (CLUBB) scheme were found to have the most impact on sea ice status and the larger Arctic climate. Our results demonstrate the importance of conducting sensitivity analyses with fully coupled climate models. The ULR configuration makes such studies computationally feasible today due to its low computational cost. When advances in computational power and modeling algorithms enable the tractable use of higher‐resolution models, our results will provide a baseline that can quantify the impact of model resolution on the accuracy of sensitivity indices. Moreover, the confidence intervals provided by our study, which we used to quantify the impact of the number of model evaluations on the accuracy of sensitivity estimates, have the potential to inform the computational resources needed for future sensitivity studies.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

On the choice of reference orbitals for linear-response calculations of solution-phase K-edge X-ray absorption spectra

The simplest response theory methods for computing vertical excitation spectra in condensed-phase are configuration interaction with single excitations (CIS) and linear-response time-dependent density functional theory (TDDFT) within the Tamm–Dancoff approximation. In applications to X-ray absorption spectroscopy (XAS), methods like CIS and TDDFT that codify only single excitations into the wave function are prone to catastrophic errors in main-edge and post-edge features whose shapes act as a crucial fingerprint in structural analyses of liquids. Here, we show that these errors manifest primarily due to a lack of orbital relaxation in conventional linear-response theories and that core-ionized (n - 1-electron) references, like those of electron-affinity TDDFT, can eliminate the errors in the spectral profile, even in the highest-energy parts of the post-edge. Crucially, we find that single excitations atop core-ionized references are sufficient to elucidate liquid-phase XAS spectra with semi-quantitative accuracy, opening the door for methods like electron-affinity CIS/TDDFT to be used as efficient alternatives to higher-order wave function approaches.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Workhorse minimally empirical dispersion-corrected density functional with tests for weakly bound systems: r 2 SCAN + rVV 10

SCAN+rVV10 has been demonstrated to be a versatile van der Waals (vdW) density functional that delivers good predictions of both energetic and structural properties for many types of bonding. Recently, the r 2 SCAN functional was devised as a revised form of SCAN with improved numerical stability. In this work, we refit the rVV10 functional to optimize the r 2 SCAN+rVV10 vdW density functional and test its performance for molecular interactions and layered materials. Our molecular tests demonstrate that r 2 SCAN+rVV10 outperforms its predecessor SCAN+rVV10 in both efficiency (numerical stability) and accuracy. This good performance is also found in lattice-constant predictions. In comparison with benchmark results from higher-level theories or experiments, r 2 SCAN+rVV10 yields excellent interlayer binding energies and phonon dispersions for layered materials.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Extensive Attention Mechanisms in Graph Neural Networks for Materials Discovery

We present our research where attention mechanism is extensively applied to various aspects of graph neural net- works for predicting materials properties. As a result, surrogate models can not only replace costly simulations for materials screening but also formulate hypotheses and insights to guide further design exploration. We predict formation energy of the Materials Project and gas adsorption of crystalline adsorbents, and demonstrate the superior performance of our graph neural networks. Moreover, attention reveals important substructures that the machine learning models deem important for a material to achieve desired target properties. Our model is based solely on standard structural input files containing atomistic descriptions of the adsorbent material candidates. We construct novel methodological extensions to match the prediction accuracy of state-of-the-art models some of which were built with hundreds of features at much higher computational cost. We show that sophisticated neural networks can obviate the need for elaborate feature engineering. Our approach can be more broadly applied to optimize gas capture processes at industrial scale.

Cong, Guojing↗

DOE OSTI2020

Real-time Electromagnetic Transient Simulation of Multi-Terminal HVDC-AC Grids based on GPU

High-fidelity electromagnetic transient (EMT) simulation plays a critical role in understanding the dynamic behavior and fast transients involved in operation, control, and protection of Multi-Terminal dc (MTdc) grids. Here, this paper proposes a cost-effective high-performance real-time EMT simulation platform for large-scale cross-continental MTdc grids based on graphics processing unit (GPU). The proposed simulation platform: i) assembles detailed EMT models of all components within an MTdc-ac grid into a single platform. This setup provides a complete simulation solution to capture fast transient signals required for high-bandwidth controller design and protection studies without any compromise; ii) implements the first GPU-based simulation architecture and corresponding algorithms for MTdc-ac grids with real-time performance at scales of 1s; iii) is highly-efficient and balances the high utilization of GPU resources and low latency required for the simulation; and iv) outperforms the existing central processing unit (CPU)- or digital signal processor (DSP)/field-programmable gate array (FPGA)-based simulators in terms of its higher scalability on large-scale MTdc-ac grids and superior price-performance ratio on the hardware. Accuracy and performance of the proposed platform are evaluated with respect to the reference results from PSCAD/EMTDC environment.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Assessment of Modeling and Nuclear Data Needs for Active Neutron Interrogation

This document is the primary deliverable for a scoping study proposed to DOE National Laboratory Announcement Number LAB 19-2114 in the NNSA research area. The study supports user applications employing active neutron interrogation by providing a science plan to improve the modeling capability and the nuclear data that radiation transport codes use. Users rely on the accuracy of the elastic scattering and non-elastic cross-sections spanning thermal energies to 14 MeV (and higher in some cases) for modeling the neutron transport through complex geometries of materials potentially composed of many elements. While the elastic scattering cross-section data are accepted for all commonly occurring elements, the non-elastic cross-section data and the associated emission data include reaction channels that require attention. The study focused on the non-elastic reactions that emit secondary, also known as prompt, gammas with the premise that many users would benefit from improved modeling of these reactions. Many users develop material assay technologies based upon gamma signatures from radiative capture, inelastic scattering, and reactions on low-Z isotopes emitting multiple particles, so the nuclear data gaps, modeling deficiencies, and recommendations for addressing the shortfalls were assessed for these reactions. Fission gammas were excluded from this study because there are other efforts underway to address known shortfalls. Follow-on efforts that successfully execute the recommendations will tangibly improve to the ability to model gamma signatures and backgrounds for user applications, such as controlled substance detection, oil-well logging, and space exploration.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Solid Phase Gradient Alloying Method via ShAPE

Alloying has been used to confer desirable properties to various metallic systems since the bronze age. For example, Ni, Mn, and Cr are alloyed into an Fe matrix to make steel, which has improved strength and corrosion resistance (as displayed in Fig. 1). In addition to the type of alloying elements, the amount of alloying elements added into a matrix is also very critical for a material’s mechanical property, physical property, machinability, and cost. Conventionally, new alloys are discovered by combining alloy precursors and elemental constituents into a mixture and melting them together. The solidified product or ingot represents a usually non-homogeneous combination of the chemistry with a microstructure dictated by the physics of solidification. It is time and energy intensive to optimize the alloying amounts via the casting process and hence, economically not conducive to rapid alloy discovery. In addition, the cast microstructure seen in the as-cast ingot is often not the ideal, nor even desired structure for optimized performance. Cast materials often need further processing such as homogenization, annealing, and deformation work put in through rolling, forging, extruding, etc. to have favorable microstructures and properties. A faster method to discover new alloy combinations and evaluate them in their worked or processed form is needed. Recently, Laser Engineered Net Shaping was applied to fabricate gradient compositional material for alloy designing. However, the mechanical properties of this gradient alloy were poor due to the existence of impurities, oxidation and cracks that are inherent in the melt-solidification process. Because of these limitations, we propose to use a solid-phase process (ShAPE) to create bulk materials (extrudates) that vary in composition from one end of the extruded solid to the other. Various manufacturing methods for alloy design including conventional casting method, laser based combinatorial method and current solid phase gradient alloying method are displayed and compared in Fig. 2. ShAPE machine and a schematic of ShAPE process are displayed in Fig. 3. Starting material is processed by a rotating and plunging die to form an extrudate. We are proposing through this methodology to invent a new, bulk-scale combinatorial technique. With this novel technique, alloying element can be dissolved into the matrix with a continued gradient without bulk melting. Formation of inter-metallics and defects originating from melt processing can be avoided. In addition, the ShAPE process creates the mixed alloy chemistry, and meanwhile subjects the material to severe plastic strain, which induces the favorable “worked” microstructure. Products from the ShAPE process can be tested in hardness directly providing a path to rapid evaluation of mechanical properties. The ability to create graded structures using friction stir processing (FSP) has been demonstrated, however using the ShAPE process we believe will lead to much faster and higher fidelity results. When combined with high throughput screening methods of physical, mechanical and microstructural property characterization, efficiency and accuracy of alloy design can be significantly improved.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Feasibility of Using Fourier Neural Operators for 3D Elastic Seismic Simulations

Seismic simulations in three-dimensional (3D) Earth models are important in many of the seismological applications related to our lab’s mission, however, accurate high-fidelity simulations are computationally costly. We investigated the capabilities of the newly developed Fourier Neural Operator (FNO) to solve the 3D elastic wave equations for seismic simulations. We generated simulation data for training the FNO model, and analyzed its performance on various test cases, such as the performance with different number of training data, different resolutions, and on more canonical structures. We found the FNO model can reproduce 3D seismic simulations with high accuracy and ~169 times faster on a small spatial grid size of 16 x 16 x16. When applied on higher resolution data, we found that transfer learning with fine-tuning on a small amount of data achieves reasonable results. This feasibility study showed promising results for using FNO’s for 3D seismic simulations and represents the foundation of further research to develop this into a more mature approach for different seismological applications. The potential impact of this project will provide techniques for large-scale or real-time applications of solving PEDs in support of national lab’s programs.

58 GEOSCIENCES↗

DOE OSTI2025

FY-25 Progress on Computational Modeling of the Water Based NSTF

This report summarizes the system level modeling using RELAP5-3D of the Natural Convection Shutdown Heat Removal Test Facility (NSTF) completed in FY25. This year’s work focuses on a new tank configuration where the inlet of the tank was lowered in elevation by 45”. The stability boundaries of the NSTF are thoroughly studied and stability maps are constructed based on the stability and the oscillation patterns of the system. Five distinct operational modes are identified, namely single-phase liquid, uniform double peak oscillations, uniform sinusoidal oscillations, stable two-phase flow, and non-uniform oscillations. Next, the riser inlet throttling case of experimental test Run-104 is simulated with the RELAP5 model where good agreement is obtained between the model and the experimental data. The simulation also highlights the effects of backflow of water from the tank to the upper region of the chimney. Additionally, the decay heat removal test of Run-99 is simulated with the RELAP5 model. Comparison is carried out between this run and a similar run with the mid-tank inlet of Run-74 performed in FY22. With the lower tank inlet, the RELAP5 model is able to predict the experimental data more accurately than the previous mid tank inlet configuration. The discrepancy in model prediction accuracy highlights the non-symmetrical spatial effects in the tank that would otherwise be more easily captured with higher fidelity models. Lastly, two exploratory studies are conducted to investigate the behaviors of the NSTF when 1) heating is provided to the downcomer and 2) a bypass channel is added between the horizontal chimney section to the downcomer.

42 ENGINEERING↗

DOE OSTI2025

Expansion of the Direct Feed High-Level Waste Glass Composition in the High Al Range

Baseline glass compositions have been developed and demonstrated for successful immobilization of Hanford high-level waste (HLW) prepared through a pretreatment process. Recent enhanced waste glass formulations have shown promise to increase the waste loading of pretreated sludge compositions from a broader range of HLW feeds. This project proposes to increase the loading of minimally pretreated Hanford HLW in glass by expanding the existing database and glass property-composition models. Estimated direct-feed high level waste (DFHLW) compositions were generated by the Hanford Tank Operations Contractor and used by Pacific Northwest National Laboratory to determine target glass compositions. Gaps in existing data were identified including one high-priority gap in the high Al compositional region. This report summarizes the data collected during the characterization of the DFHLW High Al Glass Matrix. These glasses were intentionally designed with high aluminum concentrations (15 to 30 wt%) and a high likelihood of nepheline formation, which is known to negatively affect glass durability. Some glasses were expected to either fail or approach property constraints to fill data gaps in poorly understood regions of the compositional space due to lack of data. Out of the 50 glasses tested, 14 glasses formed nepheline, while the model predicted nepheline formation in 20 glasses. All quenched glasses met the product consistency test durability constraint; however, 8 glasses failed this constraint after undergoing the canister centerline cooling treatment. Additionally, 17 glasses did not meet the viscosity constraints, 4 failed the EC constraints, and 2 exceeded the allowable T2% for spinel crystal formation. All glasses satisfied the SO 3 solubility limit. The resulting dataset provides valuable information to improve model accuracy and reduce prediction uncertainty. These insights will ultimately support the development of more robust glass formulation strategies, enabling higher waste loadings, reducing operational risks, and expanding the processing envelope.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2022

Improving the accuracy of discretisations of the vector transport equation on the lowest-order quadrilateral Raviart-Thomas finite elements

Within finite element models of fluids, vector-valued fields such as velocity or momentum variables are commonly discretised using the Raviart-Thomas elements. However, when using the lowest-order quadrilateral Raviart-Thomas elements, standard finite element discretisations of the vector transport equation typically have a low order of spatial accuracy. This paper describes two schemes that improve the accuracy of transporting such vector-valued fields on two-dimensional curved manifolds. The first scheme that is presented reconstructs the transported field in a higher-order function space, where the transport equation is then solved. The second scheme applies a mixed finite element formulation to the vector transport equation, simultaneously solving for the transported field and its vorticity. In this work, an approach to stabilising this mixed vector-vorticity formulation is presented that uses a Streamline Upwind Petrov-Galerkin (SUPG) method. These schemes are then demonstrated, along with their accuracy properties, through some numerical tests. Two new test cases are used to assess the transport of vector-valued fields on curved manifolds, solving the vector transport equation in isolation. The improvement of the schemes is also shown through two standard test cases for rotating shallow-water models.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

An open-source framework for balancing computational speed and fidelity in production cost models

Studies of bulk power system operations need to incorporate uncertainty and sensitivity analyses, especially around exposure to weather and climate variability and extremes, but this remains a computational modeling challenge. Commercial production cost models (PCMs) have shorter runtimes, but also important limitations (opacity, license restrictions) that do not fully support stochastic simulation. Open-source PCMs represent a potential solution. They allow for multiple, simultaneous runs in high-performance computing environments and offer flexibility in model parameterization. Yet, developers must balance computational speed (i.e. runtime) with model fidelity (i.e. accuracy). In this paper, we present Grid Operations (GO), a framework for instantiating open-source, scale-adaptive PCMs. GO allows users to search across parameter spaces to identify model versions that appropriately balance computational speed and fidelity based on experimental needs and resource limits. Results provide generalizable insights on how to navigate the fidelity and computational speed tradeoff through parameter selection. We show that models with coarser network topologies can accurately mimic market operations, sometimes better than higher-resolution models. It is thus possible to conduct large simulation experiments that characterize operational risks related to climate and weather extremes while maintaining sufficient model accuracy.

42 ENGINEERING↗

DOE OSTI2022

Predicting Local Material Thickness from Steady-State Ultrasonic Wavefield Measurements Using a Convolutional Neural Network

Acoustic steady-state excitation spatial spectroscopy (ASSESS) is a full-field, ultrasonic non-destructive evaluation (NDE) technique used to locate and characterize defects in plate-like structures. ASSESS generates a steady-state, single-tone ultrasonic excitation in a structure and a scanning laser Doppler Vibrometer (LDV) measures the resulting full-field surface velocity response. Traditional processing techniques for ASSESS data rely on wavenumber domain analysis. This paper presents the alternative use of a convolutional neural network (CNN), trained using simulated ASSESS data, to predict the local plate thickness at every pixel in the wavefield measurement directly. The defect detection accuracy of CNN-based thickness predictions are shown to improve for defects of greater size, and for defects with higher thickness reductions. The CNN demonstrates the ability to predict thickness accurately in regions where Lamb wave dispersion relations are complex or unknown, such as near the boundaries of a test specimen, so long as the CNN is trained on data that accounts for these regions. The CNN also shows generalizability to ASSESS experimental data, despite an entirely simulated training dataset.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗