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All sources NASA NTRS DOE OSTI

DOE OSTI2025

Utilization of Data Augmentation Techniques in Automated Inspection Systems for Defect Detection in Metals With Limited Data

Accurate identification of defects on metal surfaces is of great interest to many industry sectors, such as the automotive and aerospace industries. In contrast to conventional manual inspection techniques, recent automated inspection systems employ deep learning models trained to detect defects rapidly and precisely. The development of these models often requires a substantial image dataset to acquire adequate knowledge of defect features and enhance their predictive accuracy. When data is limited, augmentation techniques are often used to improve the precision and accuracy of defect detection systems. This study examined the prediction performance of two object detection models, namely Faster Region‐based Convolutional Neural Network (Faster R‐CNN) and You Only Look Once version 8 (YOLOv8), to identify dent defects in limited images of cast iron cylinder head surfaces. The original image set contains 46 images with 563 dents. To overcome limited data availability, common image augmentation techniques along with a copy‐paste method were applied. Results show that standard augmentation improved YOLOv8 accuracy by 8.00% and average precision (AP) by 3.00%. On the other hand, the copy‐paste technique achieved a 20.00% increase in accuracy and a 1% increase in AP with just 200 synthetic dents. Furthermore, these results provide support for using the copy‐paste augmentation strategy to enhance defect detection performance, with a limited dataset, contributing to more accurate defect identification in remanufacturing processes.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Crack detection in fuel cell electrodes using a spatial filtering technique for overcoming noisy backgrounds

Image processing is a powerful tool that allows for rapid and automated data parsing in settings that occupy large variable spaces and require large data sets. Feature detection on difficultly discerned backgrounds is a subset of image processing that facilitates the extraction of quantitative metrics from otherwise subjective data. Crack detection and quantification is an important capability in polymer electrolyte membrane fuel cell quality control, failure analysis, and optimization. This work presents a technique to perform crack detection and quantification which overcomes challenges faced by commonly used image segmentation techniques. We demonstrate the use of a geometrically filtered noise‐level detection technique to select a binary threshold value from which we then quantify how cracked a sample is. Furthermore, we demonstrate the accuracy of our technique using programmatically generated test images of known crack amounts and their performance on real‐world fuel cell catalyst layer samples.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2025

A Review of the Influence of Processing Parameters on ODS Steels Produced via Additive Manufacturing Techniques

Abstract This paper reviews current observations regarding processing conditions for oxide dispersion-strengthened steels consolidated through additive manufacturing techniques. Variations in ODS steels observed across process parameters include changes in grain size, grain texture, oxide size, density of oxides, porosity, melt pool characteristics, and mechanical properties. These properties were then compared across techniques to understand which techniques and processing conditions lead to the highest strength, ductility, and oxide density. Current literature suggests that a mix of grain types, in the form of either morphology or phase, can significantly increase the strength of printed ODS steels. Meanwhile, the most ductile samples, regardless of consolidation technique or matrix material, were made from feedstock with oxide additions located on the powder surface. Reported grain and oxide sizes were plotted against the ratio of laser power to scan speed, volumetric energy density, and normalized enthalpy. No strong correlation between these values and microstructural features was observed. The plots that were made suggest that a larger data set, more in-depth representative equations, and more defined material properties as a function of specific feedstock used are necessary to determine a value that can be correlated to the printed ODS steel microstructure.

deJong, Matthew↗

DOE OSTI2020

Improved sampling technique to collect natural gas from hydrate-bearing pressure cores

High quality gas compositional data are an important factor in interpreting the genetic source of natural gas hosted in hydrate-bearing sediments and other subsurface systems. In order to accurately characterize the composition of gas samples degassed from hydrate-bearing pressure cores, one must use a reproducible sampling technique that minimizes artifacts of the sampling process. Herein, we review sediment core degassing techniques and compare data obtained from a commonly used degassing approach, which we term the standard quantitative degassing (SQD) technique, to our newly developed modified quantitative degassing (MQD) method designed to minimize atmospheric contamination and gas-water interactions. The SQD method allows sample gas to interact with water in a bubbling chamber, which we hypothesize could alter the gas composition following mixing with water or dissolved gases in the bubbling chamber. Whereas, the MQD method allows for the collection of sample gas prior to the bubbling chamber. To compare the SQD and MQD methods, we performed a side-by-side comparison of noble (He, Ne, Ar, Kr, and Xe), major (H 2 , N 2 , O 2 , and CO 2 ), and hydrocarbon (CH 4 , C 2 H 6 , C 3 H 8 , i-C 4 H 10 , C 4 H 10 , i-C 5 H 12 , C 5 H 12 ) gas concentrations and select isotopic compositions obtained using both sample collection techniques. Gas samples were collected from hydrate-bearing pressure cores recovered and maintained under hydrate stable conditions from the northern Gulf of Mexico during the UT-GOM 2 -1 Expedition. The MQD method displayed significantly lower concentrations of atmospheric gases, higher proportions of hydrocarbon gases, lower ratios of C 1 /C 2 + , and heavier stable carbon and hydrogen isotopes of methane than the SQD method. These results demonstrate that the MQD method reduced air contamination and minimized alteration of the hydrocarbon gases. Finally, we conclude this method may be important for future work that seeks to determine the composition of natural gas from pressure cores using quantitative degassing experiments, especially those seeking to measure major (e.g., N 2 ) and noble gases.

58 GEOSCIENCES↗

DOE OSTI2023

Exploratory analysis of machine learning techniques in the Nevada geothermal play fairway analysis

Play fairway analysis (PFA) is commonly used to generate geothermal potential maps and guide exploration studies, with a particular focus on locating and characterizing blind geothermal systems. This study evaluates the application of machine learning techniques to PFA in the Great Basin region of Nevada. Following the evaluation of various techniques, we identified two approaches to PFA that produced promising results, 1) supervised Bayesian probabilistic neural networks to generate geothermal potential maps with confidence intervals, and 2) unsupervised principal component analysis paired with k-means clustering to generate both cluster maps to help identify spatial patterns, as well as new combined feature inputs. We applied these techniques to perform a comparative analysis between two principal sets of geological and geophysical features related to permeability and heat and a set of positive (known geothermal resources) and negative training sites (known drill sites with unsuitable geothermal conditions). We found that these methods constrain previously unrecognized feature controls on geothermal favorability, many of which are spatially organized within the extent of cluster groups and the major structural-hydrologic domains of the study area. Furthermore, we utilized exploratory unsupervised modeling to highlight spatial relationships between input data and predictive output results of our supervised modeling. As a result, we demonstrate how our models compare to the previous Nevada PFA and how the rapid insights these machine learning techniques offer may support future assessments of both known and undiscovered blind geothermal systems in the Great Basin region of Nevada and beyond.

15 GEOTHERMAL ENERGY↗

DOE OSTI2023

Growth interface study of CdTeSe crystals grown by the THM technique

The Traveling Heater Method (THM) is a widely accepted technique to grow high-quality detector grade CdTe and CdZnTe ingots, especially for X-ray and gamma-ray detector applications. Unlike melt-growth techniques, the growth interface for ingots produced by the THM technique consists of two different compounds, viz. the solidified region (just below the interface) consists of the compound to be grown (e.g., CdZnTe for CdZnTe growth), while the molten zone just above the growth interface consists of Te-rich CdZnTe. Thus, optimization of the growth parameters is critical to obtain a clean growth interface with the required shape and presence of minimal Te-rich inclusions. Here, in this study, CdTeSe ingots were grown employing the THM technique to investigate the growth interface. Both macroscopic and microscopic behavior of the interface were studied. The study revealed that a microscopically smooth growth interface can be achieved by optimizing the growth parameters, which is essential for obtaining high-quality, inclusion-free ingots.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2025

An overview of HR-EBSD techniques for mapping local stress and dislocations in crystalline materials at sub-micron resolution

High resolution electron backscatter diffraction (HR-EBSD) is a technique used to map elastic strain, crystallographic orientation and dislocation density in a scanning electron microscope. Here, this review covers the background and mathematics of this technique, contextualizing it within the broader landscape of EBSD techniques and other materials characterization methods. Several case studies are presented showing the application of HR-EBSD to the study of plasticity in metals, failure analysis in microelectronics and defect quantification in thin films. This is intended to be a comprehensive resource for researchers developing this technique as well as an introduction to those wishing to apply it.

Ruggles, Timothy J. [Sandia National Laboratories ↗

DOE OSTI2022

Toward reducing uncertainty quantification costs in DEM models of particulate flow: Testing simple, sensitivity-based, forward uncertainty propagation techniques

The performance of two conceptually-simple uncertainty quantification techniques are tested against the rigorous nested-loop sampling technique of Roy and Oberkampf (Comput Methods Appl Mech Eng, 200: 2131–2144, 2011) (herein called full-sampling) using two very small-scale DEM-based models of particulate flow (one gas-solid flow and one granular flow). The first simplified forward uncertainty propagation technique, reduced-sampling, uses a sensitivity analysis to eliminate uncertain inputs that have little impact on the model output prior to nested-loop sampling. The second technique, boundary-sampling, uses a sensitivity analysis to inform the selection of two bounding cases for each key model output. In conclusion, the uncertainties in the model outputs obtained via the reduced- and boundary-sampling methods agree well with those from full-sampling for both the gas-solid and granular flow models while yielding computational savings of 65–75% (reduced sampling) and 94–97% (boundary sampling).

42 ENGINEERING↗

DOE OSTI2020

Review of heat transfer enhancement techniques for single phase flows

The thermal energy exchange between a flowing fluid and its confining channel is a ubiquitous process in modern society. To enhance the fluid-to-wall or wall-to-fluid heat transfer, several techniques have been developed to maximize the contact area between the fluid and the inner wall and/or disrupt the flow to enhance circulation or induce turbulence. Deployment of channels having features capable of enhancing heat transfer enables the reduction of heat exchanger size while maintaining performance. Reduction in equipment size is critical due to the ability to minimize the required volume of costly working fluids and to mitigate potential safety concerns associated with total system fluid volume. Here, a comprehensive review of single-phase heat transfer enhancement techniques is presented. The article provides a thorough comparison by analyzing the heat transfer rate, pressure drop, and other operational aspects. Single-phase heat transfer enhancement methods are divided into active and passive techniques. Active methods such as electrohydrodynamic (EHD), magnetohydrodynamics (MHD), or mechanical motion require external power to create enhancement. Passive methods such as dimples, fins, or tape inserts do not require external input and rely only on surface modification. Although active methods are more expensive and difficult to implement compared to passive techniques, it enables active control of heat transfer augmentation. Finally, this review develops and summarizes key learning data for design optimization enabled by additive manufacturing and machine learning algorithms, helping to inform these next-generation heat exchanger design methodologies for a plethora of modern applications such as electrification of vehicles, computing, and classical industries.

42 ENGINEERING↗

DOE OSTI2023

A survey of techniques for optimizing transformer inference

Recent years have seen a phenomenal rise in the performance and applications of transformer neural networks. The family of transformer networks, including Bidirectional Encoder Representations from Transformer (BERT), Generative Pretrained Transformer (GPT) and Vision Transformer (ViT), have shown their effectiveness across Natural Language Processing (NLP) and Computer Vision (CV) domains. Transformer-based networks such as ChatGPT have impacted the lives of common men. However, the quest for high predictive performance has led to an exponential increase in transformers' memory and compute footprint. Researchers have proposed techniques to optimize transformer inference at all levels of abstraction. Further, this paper presents a comprehensive survey of techniques for optimizing the inference phase of transformer networks. We survey techniques such as knowledge distillation, pruning, quantization, neural architecture search and lightweight network design at the algorithmic level. We further review hardware-level optimization techniques and the design of novel hardware accelerators for transformers. We summarize the quantitative results on the number of parameters/FLOPs and the accuracy of several models/techniques to showcase the tradeoff exercised by them. We also outline future directions in this rapidly evolving field of research. We believe that this survey will educate both novice and seasoned researchers and also spark a plethora of research efforts in this field.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Understanding the Structure and Dynamics of Complex Biomembrane Interactions by Neutron Scattering Techniques

The membrane is one of the key structural materials of biology at the cellular level. Composed predominantly of a bilayer of lipids with embedded and bound proteins, it defines the boundaries of the cell and many organelles essential to life and therefore is involved in almost all biological processes. Membrane-specific interactions, such as drug binding to a membrane receptor or the interactions of an antimicrobial compound with the lipid matrix of a pathogen membrane, are of interest across the scientific disciplines. Herein we present a review, aimed at nonexperts, of the major neutron scattering techniques used in membrane studies: small-angle neutron scattering, neutron membrane diffraction, neutron reflectometry, quasielastic neutron scattering, and neutron spin echo. Neutron scattering techniques are well suited to studying biological membranes. The nondestructive nature of cold neutrons means that samples can be measured for long periods without fear of beam damage from ultraviolet, electron, or X-ray radiation, and neutron beams are highly penetrating, thus offering flexibility in samples and sample environments. Most important is the strong difference in neutron scattering lengths between the two most abundant forms of hydrogen, protium and deuterium. Changing the relative amounts of protium/deuterium in a sample allows the production of a series of neutron scattering data sets, enabling the observation of differing components within complex membrane architectures. This approach can be as simple as using the naturally occurring neutron contrast between different biomolecules to study components in a complex by changing the solution H 2 O/D 2 O ratio or as complex as selectively labeling individual components with hydrogen isotopes. This review presents an overview of each experimental technique with the neutron instrument configuration, related sample preparation and sample environment, and data analysis, highlighted by a special emphasis on using prominent neutron contrast to understand structure and dynamics. This review gives researchers a practical introduction to the often enigmatic suite of neutron beamlines, thereby lowering the barrier to taking advantage of these large-facility techniques to achieve new understandings of membranes and their interactions with other molecules.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

A Monte Carlo technique to model performance of streak camera-based time-resolving x-ray spectrometers

A Monte Carlo technique has been developed to simulate the expected signal and the statistical noise of x-ray spectrometers that use streak cameras to achieve the time resolution required for ultrafast diagnostics of laser-generated plasmas. The technique accounts for statistics from both the photons incident on the streak camera’s photocathode and the electrons emitted by the photocathode travelling through the camera’s electron optics to the sensor. We use the technique to optimize the design of a spectrometer, which deduces the temporal history of electron temperature of the hotspot in an inertial confinement fusion implosion from its hard x-ray continuum emission spectra. The technique is general enough to be applied to any instrument using an x-ray streak camera.

Stoupin, S. (ORCID:0000000226225270)↗

DOE OSTI2024

Enhanced sensitivity to target offset when using cross-beam energy transfer mitigation techniques in direct-drive inertial confinement fusion implosions

In direct-drive inertial confinement fusion, target offset from the target chamber center (or center of beam convergence) may lead to significant implosion asymmetry and fusion yield degradation. In addition, cross-beam energy transfer (CBET) has been shown to be a significant source of laser energy scattering and leads to a reduction in implosion velocity and yield. To improve energy coupling and implosion performance, several techniques for CBET mitigation have been proposed. Recent simulations, however, have shown that CBET also substantially mitigates the effect of target offset on implosion asymmetry and yield [Anderson et al., Phys. Plasmas 27, 112713 (2020)]. Furthermore, the inclusion of CBET models in radiation-hydrodynamics codes was shown to greatly improve agreement between simulations and experiments involving substantial target offset distances. This paper explores the intensity dependence of this CBET–offset effect. In addition, it is shown that enhanced sensitivity to target offset can be expected when CBET-mitigation techniques are used in direct-drive implosions. This is shown through simulations of two such CBET-mitigation techniques on the OMEGA laser: (1) decreased beam-to-target radius, and (2) beam-to-beam frequency detuning. For the typical target offset distances (<15 μm) observed in experiments on OMEGA, however, overall yield is still anticipated to be substantially higher when CBET-mitigation techniques are employed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Transmission interference fringe ( TIF ) technique for the dynamic visualization of evaporating droplet

Here, the transmission interference fringe (TIF) technique was developed to visualize the dynamics of evaporating droplets based on the Reflection Interference Fringe (RIF) technique for micro-sized droplets. The geometric formulation was conducted to determine the contact angle (CA) and height of macro-sized droplets without the need for the prism used in RIF. The TIF characteristics were analyzed through experiments and simulations to demonstrate a wider range of contact angles from 0 to 90°, in contrast to RIF's limited range of 0–30°. TIF was utilized to visualize the dynamic evaporation of droplets in the constant contact radius (CCR) mode, observing the droplet profile change from convex-only to convex-concave at the end of dry-out from the interference fringe formation. The TIF also observed the contact angle increase from the fringe radius increase. This observation is uniquely reported as the interference fringe (IF) technique can detect the formation of interference fringe between the reflection from the center convex profile and the reflection from the edge concave profile on the far-field screen. Unlike general microscopy techniques, TIF can detect far-field interference fringes as it focuses beyond the droplet-substrate interface. The formation of the convex-concave profile during CCR evaporation is believed to be influenced by the non-uniform evaporative flux along the droplet surface.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

Machine learning techniques for intermediate mass gap lepton partner searches at the large hadron collider

We consider machine learning techniques associated with the application of a boosted decision tree (BDT) to searches at the Large Hadron Collider (LHC) for pair-produced lepton partners which decay to leptons and invisible particles. This scenario can arise in the minimal supersymmetric Standard Model (MSSM), but can be realized in many other extensions of the Standard Model (SM). We focus on the case of intermediate mass splitting ( ∼ 30 GeV ) between the dark matter (DM) and the scalar. For these mass splittings, the LHC has made little improvement over LEP due to large electroweak backgrounds. We find that the use of machine learning techniques can push the LHC well past discovery sensitivity for a benchmark model with a lepton partner mass of ∼ 110 GeV , for an integrated luminosity of 300 fb − 1 , with a signal-to-background ratio of ∼ 0.3 . The LHC could exclude models with a lepton partner mass as large as ∼ 160 GeV with the same luminosity. The use of machine learning techniques in searches for scalar lepton partners at the LHC could thus definitively probe the parameter space of the MSSM in which scalar muon mediated interactions between SM muons and Majorana singlet DM can both deplete the relic density through dark matter annihilation and satisfy the recently measured anomalous magnetic moment of the muon. We identify several machine learning techniques which can be useful in other LHC searches involving large and complex backgrounds. Published by the American Physical Society 2024

Astronomy & Astrophysics↗

DOE OSTI2025

A Polar Scaling Technique for the Regularization of Strongly Singular and Strongly Near-Singular Helmholtz Surface Integrals Evaluated Over 2-D Domains

The numerical integration of expressions containing strong singularities or strong near-singularities has long been a challenging problem in the electromagnetics community. Much attention has been paid to this problem, as strong $1/R^{{2}}$ singularities routinely appear when implementing electromagnetic simulation techniques like the method of moments (MoM). To date, several techniques, from singularity extraction to singularity cancellation (SC), have been employed to deal with problems that require the evaluation of 2-D strongly singular integrals. However, no single technique has been proposed that can deal with both strong singularities and strong near-singularities in a fully numerical manner for arbitrary 2-D domains. Moreover, it has been claimed that the Helmholtz-type strongly singular integral found in the MoM is convergent in a principal value sense, but this convergence value has yet to be proven mathematically. In this work, we will conduct the convergence proof and introduce a “polar scaling” change of variables method that may be used to evaluate Helmholtz integrals with both strong and weak singularities/near-singularities. The technique is fully numerical and can in principle be applied to any planar or curved polygon and any nonsingular basis function. We will also provide numerical results showing useful convergence behavior for integrals involving both exact and near-singularities.

47 OTHER INSTRUMENTATION↗

DOE OSTI2025

Scalable Hybrid Learning Techniques for Scientific Data Compression

Data compression is becoming critical for storing scientific data because many scientific applications need to store large amounts of data and post process this data for scientific discovery. Unlike image and video compression algorithms that limit errors to primary data (PD), scientists require compression techniques that accurately preserve derived quantities of interest (QoIs). Here, this article presents a physics-informed compression technique implemented as an end-to-end, scalable, GPU-based pipeline for data compression that addresses this requirement. Our hybrid compression technique combines machine learning techniques and standard compression methods. Specifically, we combine an autoencoder, an error-bounded lossy compressor to provide guarantees on raw data error, and a constraint satisfaction post-processing step to preserve the QoIs within a minimal error (generally less than floating point error). The effectiveness of the data compression pipeline is demonstrated by compressing nuclear fusion simulation data generated by a large-scale fusion code, XGC, which produces hundreds of terabytes of data in a single day. Our approach works within the ADIOS framework and results in compression by a factor of more than 150 while requiring only a few percent of the computational resources necessary for generating the data, making the overall approach highly effective for practical scenarios.

ITER↗

DOE OSTI2024

High‐speed 4‐dimensional scanning transmission electron microscopy using compressive sensing techniques

Abstract Here we show that compressive sensing allows 4‐dimensional (4‐D) STEM data to be obtained and accurately reconstructed with both high‐speed and reduced electron fluence. The methodology needed to achieve these results compared to conventional 4‐D approaches requires only that a random subset of probe locations is acquired from the typical regular scanning grid, which immediately generates both higher speed and the lower fluence experimentally. We also consider downsampling of the detector, showing that oversampling is inherent within convergent beam electron diffraction (CBED) patterns and that detector downsampling does not reduce precision but allows faster experimental data acquisition. Analysis of an experimental atomic resolution yttrium silicide dataset shows that it is possible to recover over 25 dB peak signal‐to‐noise ratio in the recovered phase using 0.3% of the total data. Lay abstract : Four‐dimensional scanning transmission electron microscopy (4‐D STEM) is a powerful technique for characterizing complex nanoscale structures. In this method, a convergent beam electron diffraction pattern (CBED) is acquired at each probe location during the scan of the sample. This means that a 2‐dimensional signal is acquired at each 2‐D probe location, equating to a 4‐D dataset. Despite the recent development of fast direct electron detectors, some capable of 100kHz frame rates, the limiting factor for 4‐D STEM is acquisition times in the majority of cases, where cameras will typically operate on the order of 2kHz. This means that a raster scan containing 256^2 probe locations can take on the order of 30s, approximately 100‐1000 times longer than a conventional STEM imaging technique using monolithic radial detectors. As a result, 4‐D STEM acquisitions can be subject to adverse effects such as drift, beam damage, and sample contamination. Recent advances in computational imaging techniques for STEM have allowed for faster acquisition speeds by way of acquiring only a random subset of probe locations from the field of view. By doing this, the acquisition time is significantly reduced, in some cases by a factor of 10‐100 times. The acquired data is then processed to fill‐in or inpaint the missing data, taking advantage of the inherently low‐complex signals which can be linearly combined to recover the information. In this work, similar methods are demonstrated for the acquisition of 4‐D STEM data, where only a random subset of CBED patterns are acquired over the raster scan. We simulate the compressive sensing acquisition method for 4‐D STEM and present our findings for a variety of analysis techniques such as ptychography and differential phase contrast. Our results show that acquisition times can be significantly reduced on the order of 100‐300 times, therefore improving existing frame rates, as well as further reducing the electron fluence beyond just using a faster camera.

Robinson, Alex W.↗