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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Assessment of Weld Microstructure Through Sound Velocity Measurements for Power Plant Applications

Here, this research study employed an immersion-based micro-resolution ultrasonic velocity measurement technique (MUVT) to evaluate Grade 91 (9Cr-1Mo-V) steel welds. This process utilized a 20 MHz focused ultrasonic beam with a spot size of 250 µm, a 6 µm laser vibrometer spot size for detection, and an automated 3-axis raster scanner to accurately collect ultrasonic velocity data from two Grade 91 steel welds fabricated using cold metal transfer (CMT) and flux-cored arc welding (FCAW) processes. By analyzing the MUVT data, the Poisson’s ratio (υ) and modulus of elasticity (E), which are important mechanical properties for weldments, were calculated. The correlation between mechanical properties and ultrasonic velocity was examined. The results indicated that longitudinal ultrasonic velocity could effectively identify base metal, heat-affected zone (HAZ), and weld metal areas, showing a strong correlation with hardness. In particular, the distinctive V-shaped dip in velocity and hardness data across the HAZ areas of both samples highlighted changes in the microstructure of creep strength–enhanced ferritic steel welds. Importantly, the immersion-based MUVT demonstrated high accuracy in small sections of the weld, surpassing the conventional contact ultrasonic testing method in spatial resolution detection.

arc welding↗
DOE OSTI2023

LEAD - LEArning Damage

LEAD is a suite of scripts to enable rapid estimation of model parameters for damage models for ductile materials using machine learning. In its current version, the only supported damage model is TEPLA, though others are planned. The data we train on are comprised of free surface velocity data as well as porosity data. Some details about our method can be found in the preprint LA-UR-23-20468 (arxiv.org/abs/2301.07790)

Blaschke, Daniel↗
DOE OSTI2025

A New Vehicle-to-Vehicle Communication System: Visual-Enhanced Cooperative Traffic Operations

The advent of Connected and Autonomous Vehicles (CAVs) has highlighted the necessity for robust communication systems between vehicles and their environment. This study introduces a novel vehicle-to-vehicle (V2V) communication system, termed the Visual-Enhanced Cooperative Traffic Operations (VECTOR) system. The VECTOR system addresses the need for robust communication by converting dynamic data (including velocity and yaw angle data) into binary code, which is displayed on an LED panel mounted on the top of the vehicle. Following vehicles detect this panel and decode the information using a camera, implementing a visual-based communication method. VECTOR system employs a comprehensive five-module process. Initially, polynomial fitting techniques are applied to velocity data over fixed time intervals using third-degree polynomials, with validation via R² and MSE metrics. The second module converts velocity and yaw angle data into binary form, thereby enhancing detection and processing efficiency. The third module focuses on improving detection stability across various environmental conditions to enhance traffic safety. The fourth module decodes the binary data back into trajectory information, ensuring the fidelity of velocity and yaw angles. The final module integrates eco-control through the VECTOR system, employing advanced control algorithms to minimize energy consumption in CAVs. Experimental evaluations conducted using a modified CAV test platform based on the Lincoln MKZ demonstrate the feasibility and efficiency of the VECTOR system, achieving a 75% R-squared accuracy rate in replicating original velocity data. This methodology not only highlights potential applications but also underscores significant implications for advancing CAV technology.

Ma, Ke↗
DOE OSTI2021

Scheduling strategies for the ESPRESSO follow-up of TESS targets

ABSTRACT Radial-velocity follow-up of stars harbouring transiting planets detected by TESS is expected to require very large amounts of expensive telescope time in the next few years. Therefore, scheduling strategies should be implemented to maximize the amount of information gathered about the target planetary systems. We consider myopic and non-myopic versions of a novel uniform-in-phase scheduler, as well as a random scheduler, and compare these scheduling strategies with respect to the bias, accuracy and precision achieved in recovering the mass and orbital parameters of transiting and non-transiting planets. This comparison is carried out based on realistic simulations of radial-velocity follow-up with ESPRESSO of a sample of 50 TESS target stars, with simulated planetary systems containing at least one transiting planet with a radius below 4R⊕. Radial-velocity data sets were generated under reasonable assumptions about their noise component, including that resulting from stellar activity, and analysed using a fully Bayesian methodology. We find the random scheduler leads to a more biased, less accurate, and less precise, estimation of the mass of the transiting exoplanets. No significant differences are found between the results of the myopic and non-myopic implementations of the uniform-in-phase scheduler. With only about 22 radial velocity measurements per data set, our novel uniform-in-phase scheduler enables an unbiased (at the level of 1 per cent) measurement of the masses of the transiting planets, while keeping the average relative accuracy and precision around 16 per cent and 23 per cent, respectively. The number of non-transiting planets detected is similar for all the scheduling strategies considered, as well as the bias, accuracy and precision with which their masses and orbital parameters are recovered.

Cabona, L.↗
DOE OSTI2024

Introduction

This report provides a comprehensive overview of metadata to describe sensor signals in wastewater treatment plants and methods to obtain such metadata. In this introduction, we explain the original motivation behind the MetaCO task group. This includes a description of historical challenges (data volume, data velocity) for which mature technology is now available, and newer challenges, which relate to data structure (data variety) and data quality (veracity). We conclude the chapter with an expression of gratitude to all involved.

Aguado, Daniel↗
DOE OSTI2021

dlmcfpt.c1

The DLMC VAP uses inertial data from the ARM NAV system to transform the Doppler lidar beam angles (az and el) from the lidar's frame of reference to an Earth-fixed frame such the azimuth angle is measured clockwise from true north and the elevation angle is measured from the local horizon. Additionally, the VAP compensates the observed radial (air) velocity data for the effects of the platform velocity (i.e. corrected_radial_velocity = observed_radial_velocity-platform_radial_velocity). The VAP was developed specifically for MOSAiC. The dlmcfpt.c1 product contains the fixed point, or staring data. The dlmcusr.c1 product contains a user defined scan which is anything the operator wants it to be.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

dlmcusr.c1

The DLMC VAP uses inertial data from the ARM NAV system to transform the Doppler lidar beam angles (az and el) from the lidar's frame of reference to an Earth-fixed frame such the azimuth angle is measured clockwise from true north and the elevation angle is measured from the local horizon. Additionally, the VAP compensates the observed radial (air) velocity data for the effects of the platform velocity (i.e. corrected_radial_velocity = observed_radial_velocity-platform_radial_velocity). The VAP was developed specifically for MOSAiC. The dlmcfpt.c1 product contains the fixed point, or staring data. The dlmcusr.c1 product contains a user defined scan which is anything the operator wants it to be.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Joint velocity and density reconstruction of the Universe with nonlinear differentiable forward modeling

Reconstructing the initial conditions of the Universe from late-time observations has the potential to optimally extract cosmological information. Due to the high dimensionality of the parameter space, a differentiable forward model is needed for convergence, and recent advances have made it possible to perform reconstruction with nonlinear models based on galaxy (or halo) positions. In addition to positions, future surveys will provide measurements of galaxies' peculiar velocities through the kinematic Sunyaev-Zel'dovich effect (kSZ), type Ia supernovae, the fundamental plane relation, and the Tully-Fisher relation. Here we develop the formalism for including halo velocities, in addition to halo positions, to enhance the reconstruction of the initial conditions. We show that using velocity information can significantly improve the reconstruction accuracy compared to using only the halo density field. Here we study this improvement as a function of shot noise, velocity measurement noise, and angle to the line of sight. We also show how halo velocity data can be used to improve the reconstruction of the final nonlinear matter overdensity and velocity fields. We have built our pipeline into the differentiable Particle-Mesh FlowPM package, paving the way to perform field-level cosmological inference with joint velocity and density reconstruction. This is especially useful given the increased ability to measure peculiar velocities in the near future.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Blood flow imaging by optimal matching of computational fluid dynamics to 4D-flow data

Three-dimensional, time-resolved blood flow measurement (4D-flow) is a powerful research and clinical tool, but improved resolution and scan times are needed. Therefore, this study aims to (1) present a postprocessing framework for optimization-driven simulation-based flow imaging, called 4D-flow High-resolution Imaging with a priori Knowledge Incorporating the Navier-Stokes equations and the discontinuous Galerkin method (4D-flow HIKING), (2) investigate the framework in synthetic tests, (3) perform phantom validation using laser particle imaging velocimetry, and (4) demonstrate the use of the framework in vivo. An optimizing computational fluid dynamics solver including adjoint-based optimization was developed to fit computational fluid dynamics solutions to 4D-flow data. Synthetic tests were performed in 2D, and phantom validation was performed with pulsatile flow. Reference velocity data were acquired using particle imaging velocimetry, and 4D-flow data were acquired at 1.5 T. In vivo testing was performed on intracranial arteries in a healthy volunteer at 7 T, with 2D flow as the reference. Results Synthetic tests showed low error (0.4%-0.7%). Phantom validation showed improved agreement with laser particle imaging velocimetry compared with input 4D-flow in the horizontal (mean -0.05 vs -1.11 cm/s, P < .001; SD 1.86 vs 4.26 cm/s, P < .001) and vertical directions (mean 0.05 vs -0.04 cm/s, P = .29; SD 1.36 vs 3.95 cm/s, P < .001). In vivo data show a reduction in flow rate error from 14% to 3.5%. Phantom and in vivo results from 4D-flow HIKING show promise for future applications with higher resolution, shorter scan times, and accurate quantification of physiological parameters.

4D-flow MRI↗
DOE OSTI2020

UNRAVEL: A Robust Modular Velocity Dealiasing Technique for Doppler Radar

Unfold Radar Velocity (UNRAVEL) is an open-source modular Doppler velocity dealiasing algorithm for weather radars. UNRAVEL is an algorithm that does not need external reference velocity data, making it easily applicable. The proposed algorithm includes 11 core modules and 2 dealiasing strategies. UNRAVEL is an iterative algorithm. The goal is to build the dealiasing results starting with the strictest possible continuity tests in azimuth and range and, after each step, relaxing the parameters to include more results from a progressively growing number of reference points. UNRAVEL also has modules that perform 3D continuity checks. Thanks to this modular design, the number of dealiasing strategies can be expanded in order to optimize the dealiasing results. While the first driver dealiases Doppler velocity from each tilt independently from one another, the second driver also performs a three-dimensional continuity check of the velocity using successive elevations. The proposed dealiasing algorithm is tested using severe weather data from an S-band Doppler radar that have been aliased to mimic aliased radial velocity patterns that would be observed by a C-band Doppler radar. Artificially aliasing S-band data permits creation of a reference to which the performance of various dealiasing techniques can be compared. Comparisons show that UNRAVEL consistently outperforms other established dealiasing algorithms for the test period selected in this work.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

JHTDB-wind: a web-accessible large-eddy simulation database of a wind farm with virtual sensor querying

This paper introduces JHTDB-wind (https://turbulence.idies.jhu.edu/datasets/windfarms, last access: 11 November 2025), a publicly accessible database containing large-eddy simulation (LES) data from wind farms. Building on the framework of the Johns Hopkins Turbulence Database (JHTDB), which hosts direct numerical simulation (DNS) and some LES datasets of canonical turbulent flows, JHTDB-wind stores the 4D space–time history of the flow and provides users the ability to access and query the data via a web-based virtual sensor interface. The initial dataset comprises LES results from a large wind farm with 10×6 turbines, modeled using a filtered actuator line method, under conventionally neutral atmospheric conditions. These data comprise 1 h (hour) of flow field data (velocity, pressure, potential temperature deviation, subgrid-scale (SGS) eddy viscosity, and turbine forces, approximately 15 TB (terabytes) and wind turbine data – including both turbine-level operational quantities and blade-level aerodynamic quantities (approximately 1.3 TB) – stored in Zarr and Parquet formats, respectively. Data retrieval is facilitated by the giverny Python package, allowing remote users to query the database in Python or MATLAB (C and Fortran support are available for flow field data). This paper details the simulation setup and demonstrates data access through examples that analyze wind farm flow structures and turbine performance. The framework is extensible to future datasets, including the JHTDB-wind diurnal cycle simulation analyzed in Xiao et al. (2025).

17 WIND ENERGY↗
DOE OSTI2021

EXPRES. II. Searching for Planets around Active Stars: A Case Study of HD 101501

By controlling instrumental errors to below 10 cm s{sup −1}, the EXtreme PREcision Spectrograph (EXPRES) allows for a more insightful study of photospheric velocities that can mask weak Keplerian signals. Gaussian processes (GP) have become a standard tool for modeling correlated noise in radial velocity data sets. While GPs are constrained and motivated by physical properties of the star, in some cases they are still flexible enough to absorb unresolved Keplerian signals. We apply GP regression to EXPRES radial velocity measurements of the 3.5 Gyr old chromospherically active Sun-like star, HD 101501. We obtain tight constraints on the stellar rotation period and the evolution of spot distributions using 28 seasons of ground-based photometry, as well as recent Transiting Exoplanet Survey Satellite data. Light-curve inversion was carried out on both photometry data sets to reveal the spot distribution and spot evolution timescales on the star. We find that the >5 m s{sup −1} rms radial velocity variations in HD 101501 are well modeled with a GP stellar activity model without planets, yielding a residual rms scatter of 45 cm s{sup −1}. We carry out simulations, injecting and recovering signals with the GP framework, to demonstrate that high-cadence observations are required to use GPs most efficiently to detect low-mass planets around active stars like HD 101501. Sparse sampling prevents GPs from learning the correlated noise structure and can allow it to absorb prospective Keplerian signals. We quantify the moderate to high-cadence monitoring that provides the necessary information to disentangle photospheric features using GPs and to detect planets around active stars.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Characterization of turbulence under different stability conditions using lidar scanning data

The spatiotemporal scales of turbulence in the boundary layer flow vary with the static stability of the atmosphere. For example, turbulence structures in unstable conditions are often found to be large and coherent, whereas the turbulent structures in stable conditions are relatively small and sporadic. To better understand the behavior of such turbulent structures under different atmospheric stability conditions in the lower atmosphere, herein, we use scanning lidar data collected over several months during the US Department of Energy's second Wind Forecast Improvement Project (WFIP 2) in 2016. The radial velocity data from scanning lidar were gathered along the horizontal plane (PPI mode) and were analyzed using proper orthogonal decomposition (POD) and spectral energy methods. The results from these analyses show that the turbulent structures in daytime unstable conditions are different from those present during nighttime stable conditions in terms of amount of energy and coherent structures. The POD energy is distributed more evenly in all the higher spatial modes in the daytime unstable case compared to the nighttime stable case. Besides characterizing the turbulence in the flow, the lidar data were also used to compare the simulated flow field generated from the WRF-LES framework (Weather Research and Forecasting - large-eddy simulation) with the scanning lidar data. The results from both simulated and lidar scanned data show similar structures orienting the streak-like structure along the mean wind. This indicates that the streak-like structures can exist in the real atmosphere during unstable atmospheric condition cases.

Rai, Raj K.↗
DOE OSTI2025

Videos, photos, and AI-derived grain size data associated with “High-throughput AI Video Surveys Enable Reproducible Multiscale Sediment Size Mapping, with Implications for Hydrobiogeochemical Parameterization”

NOTE: The manuscript associated with this data package is currently in review. The data may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final dataset and additional metadata. This data package is associated with the manuscript “High-throughput AI Video Surveys Enable Reproducible Multiscale Sediment Size Mapping, with Implications for Hydrobiogeochemical Parameterization” under review. This data package includes five data types: 1) raw photos and videos from drone survey and walking smartphone surveys; 2) images derived from raw videos; 3) manual labeling of reference scales; 4) metadata for all images and photo resolution derived from artificial intelligence (AI) models or manual labels, 5) grain size data obtained from AI models for all photos, 6) metadata and grain size data after quality control, 7) summaries of sample efficiency for all data, and 8) computational fluid dynamics (CFD) data used to support hydro-biogeochemical (HBGC) parameter estimation. Such data is used to 1) demonstrate significant improvements in accuracy, efficiency, and quality control for grain size data collection with the help of AI models, 2) study the spatial heterogeneity of grain size and observation reproducibility based on tens of thousands of data points generated by the AI models, and 3) evaluate the impacts of grain size heterogeneity on key HBGC parameters across sediment-to-reach and hourly-to-yearly scales. In particular, the data package contains 116 folders and 179696 files. The files include 41 videos in .mov format, 64047 photos in .jpg format, 13541 video-derived photos in .png format, 12747 segmentation mask data in .tif format, 12747 segmentation data in .json format, 24771 .csv files that with metadata and grain size for each individual photo as well as water depth and velocity data from CFD and observation, 51791 .txt files of raw AI predicted labels, and 11 flight record data in .srt format. The summary for all metadata and grain size statistics information is included in “Scales_V3_NG.csv” and “Statistics_V3_NG.csv”. The summary for data that pass data quality control (QC) level 0-2 is included in “QCStatistics_V3_NG.csv”. The QC level 0 represents photos whose photo resolution is positive, excluding photos that miss reference scale. The QC level 1 means reference scale circularity uncertainty is less than 5% for smartphone images while representing photo resolution is larger than 0.44 mm/pixel for drone images. The QC level 2 means excluding photos whose grain number is less than 100, a minimum number of grains recommended by classic literature. The summary for each video’s name, length, frame rates, survey area, grain number, survey efficiency, etc. can be found in “QCSummary_V3_NG.csv”. The summary for site name, GPS coordinates, and number of images at each site can be found in “SitesSummary_V3_*.csv” files. Overall computational efficiency summary is reported in Table 4 of accompanying manuscript. Additionally, the nitrate concentration data used in this work was downloaded from an existing dataset published on ESS-DIVE (Boat-Dragged Sensor Hanford Reach.csv; Conner A. et al., 2020). We thank the United States Forest Service, Washington Department of Fish and Wildlife, Washington Department of Natural Resources, Cowiche Canyon Conservatory, Port of Benton, and the Confederated Tribes and Bands of the Yakama Nation for access to field locations where the data were collected. We also thank the Yakama Nation Tribal Council and Yakama Nation Fisheries for working with us to facilitate data collection and optimization of data usage according to their values and worldview.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Assimilating Scanning Radar Data into Cloud-Scale Models

The aim of this project is to improve the initial conditions of large eddy simulations (LES) of deep convective storms by assimilating scanning radar reflectivity and radial velocity data as well as local environmental observations. Accomplishing this goal often results in more accurate numerical forecasts of clouds and precipitation than if the background initial meteorological conditions are solely relied upon for convection initiation and intensification, as is typical of most operational and research models. Conducted in a storm-scale LES framework, these more accurate numerical analyses can serve as tool to better understand and parameterize land-atmosphere coupling, boundary layer turbulence, and cloud processes, each used for model parameterization development of convection. Our work examined the sensitivity of the numerical representation of cloud-scale wind and microphysical structures to: i) the assimilation of radiosonde observations characterizing the near-storm inflow, ii) the assimilation of radar data of varying resolution for model simulations with grid spacings of 1-km and 0.25-km, and iii) to the assimilation of three-dimensional plan-projection-indicator (PPI) and range-height indicator (RHI) radar volumes. Few studies have explored deep convective storm analyses and forecasts produced by assimilation of radar observations to cloud-scale LES. Furthermore, to our knowledge, no storm-scale DA study has explored the value of assimilating high-resolution sector RHI volumes to determine if they can improve the retrieval of the vertical kinematic and microphysical storm structure aloft over the use of traditionally utilized PPI scans.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Structural Evolution of the Hogback Monocline and Its Tectonic Significance in the San Juan Basin

The San Juan Basin is recognized as a Laramide foreland basin. It is located within the Colorado Plateau, a broad tectonic province characterized by a thick sedimentary sequence that was segmented into smaller sub basins during the Late Cretaceous to Paleogene Laramide orogeny. The Hogback Monocline lies along the northwestern margin of the San Juan Basin and is considered a Laramide-age structure formed in response to compressional stress. In this study, we interpret surface and subsurface datasets to construct a structural geological model and evaluate its tectonic significance. Through seismic data, we identify key fault and fold geometries at depth. The seismic dataset used in this study was reprocessed in depth and constrained with well log velocity data to enhance seismic imaging quality. Additionally, we performed well log correlations to identify formation tops and assess variations in basin infill and thickness geometry. A series of structural cross-sections, constructed using seismic data and a high density of boreholes, are presented to evaluate geometric variations along the structure and its evolution during basin development. Furthermore, kinematic restoration and forward modeling analyses were conducted to validate our structural interpretation. This work suggests that the Hogback Monocline formed through fault-propagation folding and flexural slip affecting the pre-Laramide sedimentary sequence under compressional stresses associated with the Laramide orogeny. This structure is interpreted as a high-angle reverse fault that influenced the geometry of the late basin infill. Additionally, monocline bending along the structure may have been controlled by fault relay systems and, in some cases, influenced by strike-slip faulting.

Reyes, Martin [New Mexico Bureau o fGeology and Mi↗
DOE OSTI2025

High-Speed and High-Quality Field Welding Repair Based on Advanced Non-Destructive Evaluation and Numerical Modeling

Creep strength-enhanced ferritic (CSEF) steels such as Grade 91 (9Cr-1Mo-V) and Grade 92 (Fe-9Cr-2W-0.5Mo) steels are widely used in the fossil-fuel-fired and nuclear power plants. The weld integrity of these steels is crucial for power plants' safe and reliable operations. Due to harsh service conditions, the steel weld can become susceptible to environmental degradation. Field welding repair is used to restore the degraded weld’s performance where a controlled temper-bead welding technique is commonly used to temper the freshly formed martensite during welding. However, knowledge of weld repairability is limited and experimental trial and error optimization to achieve desired microstructure and joint properties is expensive and time-consuming. Many existing computational models, e.g., finite element models, are limited to solving heat conduction equation and ignoring convective heat transfer due to molten metal flow. These models can result in over-prediction of peak temperatures of weld pool and heat-affected zone (HAZ), which in turn can affect the accuracy of tempering prediction. Moreover, these finite element models require an input of the deposit profiles in advance and thus limits the usability of these models. Here, a molten pool-based, multi-pass multi-layer model has been developed based on computational fluid dynamics (CFD) approach with the Volume of Fluid (VOF) method. The model calculates the bead formation, thereby eliminating the need for pre-determined bead profiles required by finite element models. For computational efficiency, a coordinate system attached to the moving heat source is utilized. A subroutine is developed to convert the temperature profiles in the reference frame stationary to the heat source to that stationary to the workpiece. The converted thermal cycles are then imported into a microstructure model to compute the tempering kinetics and resultant hardness using a Johnson-Mehl-Avrami-Kolmogorov (JMAK), and modified Grange-Baughman parameter. The modeling approach is first developed and validated on single- and multi-pass deposition of stainless steel filler metal onto a SA-533 high strength steel substrate. The models are then applied to a multi-pass V-groove repair weld of Grade 91 steel plate as well as directed energy deposition of Grade 92 steel. Non-destructive characterization of microstructures was performed on Grade 91 and 92 steel welds. Two welding processes, cold metal transfer (CMT) and flux-cored arc welding (FCAW), were investigated for the Grade 91 steel weld samples. For the Grade 92 weld samples, three different heat inputs (low, medium, and high) of gas tungsten arc welding (GTAW) were utilized to replicate traditional field welding processes. The non-destructive evaluation (NDE) method used for this research was immersion ultrasonic testing (UT) using a micro-resolution ultrasonic imaging methodology specifically designed to operate in the through-transmission configuration operating at 20 MHz of frequency. The system used a focused ultrasonic beam spot size diameter between 250-300 μm, and a 6 μm laser vibrometer spot size for detection, to produce highly defined images with longitudinal and mode-converted shear waves. From the micro-resolution ultrasonic C-scan images, three microstructural regions, i.e., weld metal (WM), HAZ, and base metal (BM), were clearly identifiable. Various levels of ultrasonic amplitudes distributed over the three regions were correlated with electron beam backscattered diffraction (EBSD) images using grain size, grain boundaries, and dislocation densities. The results showed that areas with relatively higher ultrasonic amplitude levels were associated with smaller grains and higher dislocation densities, while areas with lower amplitude levels were associated with larger grains and lower dislocation densities. In addition, ultrasonic velocity data obtained across the three different weld microstructural regions of Grade 91 test samples were correlated with optical metallographic images and hardness measurements. The results showed distinctive decreases in ultrasonic velocity and hardness over the HAZ region, where weld failures often occur during service.

36 MATERIALS SCIENCE↗
DOE OSTI2022

EdgeAI: Machine learning via direct attached accelerator for streaming data processing at high shot rate x-ray free-electron lasers

We present a case for low batch-size inference with the potential for adaptive training of a lean encoder model. We do so in the context of a paradigmatic example of machine learning as applied in data acquisition at high data velocity scientific user facilities such as the Linac Coherent Light Source-II x-ray Free-Electron Laser. We discuss how a low-latency inference model operating at the data acquisition edge can capitalize on the naturally stochastic nature of such sources. We simulate the method of attosecond angular streaking to produce representative results whereby simulated input data reproduce high-resolution ground truth probability distributions. By minimizing the mean-squared error between the decoded output of the latent representation and the ground truth distributions, we ensure that the encoding layers and resulting latent representation maintains full fidelity for any downstream task, be it classification or regression. We present throughput results for data-parallel inference of various batch sizes, some with throughput exceeding 100 k images per second. We also show in situ training below 10 s per epoch for the full encoder–decoder model as would be relevant for streaming and adaptive real-time data production at our nation’s scientific light sources.

97 MATHEMATICS AND COMPUTING↗