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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Phase Selection Rules of Multi‐Principal Element Alloys

Abstract Computational prediction of phase stability of multi‐principal element alloys (MPEAs) holds a lot of promise for rapid exploration of the enormous design space and autonomous discovery of superior structural and functional properties. Regardless of many plausible works that rely on phenomenological theory and machine learning, precise prediction is still limited by insufficient data and the lack of interpretability of some machine learning algorithms, e.g., convolutional neural network. In this work, a comprehensive approach is presented, encompassing the development of a complete dataset that contains 72 387 density functional theory calculations, as well as a predictive global phenomenological descriptor. The phase selection descriptor, based on atomic electronegativity and valence electron concentration, significantly outperforms the widely used valence electron concentration, excelling in both accuracy (with an f1 score of 63% compared to 47%) and its ability to predict the HCP phase (0.48 recall compared to 0). The comprehensive data mining on the global design space of 61 425 quaternary MPEAs made from 28 possible metals, together with the phenomenological theory and physical interpretation, will set up a solid computational science foundation for data‐driven exploration of MPEAs.

Chemistry↗
DOE OSTI2026

Precision Polishing of Spheres Via In-Situ Process Monitoring and Machine-Learning-Based Optimization

Precision polishing for high surface finish is a vital step in the making of many high-tech components for optical and semiconductor modules as well as for wide ranging experimental samples for HED science that is at the core of LLNL’s mission. In particular, the fabrication of extremely smooth and round spheres comprised of low atomic number materials (low-Z such as high-density carbon or HDC, beryllium, hydrocarbon polymers, etc.) for Inertial Confinement Fusion (ICF) is entirely reliant on specialized polishing processes to achieve the surface smoothness.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Strategies for Constructing and Operating DNA Origami Linear Actuators

Abstract Linear actuators are ubiquitous components at all scales of engineering. DNA nanotechnology offers a unique opportunity for bottom‐up assembly at the molecular scale, providing nanoscale precision with multiple methods for constructing and operating devices. In this paper, DNA origami linear actuators with up to 200 nm travel, based on a rail threading a topologically locked slider, are demonstrated. Two strategies, one‐ and two‐pot assembly, are demonstrated whereby the two components are folded from one or two DNA scaffold strands, respectively. In order to control the position of the slider on the rail, the rail and the inside of the slider are decorated with single‐stranded oligonucleotides with distinct sequences. Two positioning strategies, based on diffusion and capture of signaling strands, are used to link the slider reversibly to determined positions on the rail with high yield and precision. These machine components provide a basis for applications in molecular machinery and nanoscale manufacture including programmed chemical synthesis.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Utilizing machine learning to improve the precision of fluorescence imaging of cavity-generated spin squeezed states

We present a supervised learning model to calibrate the photon collection rate during the fluorescence imaging of cold atoms. The linear regression model finds the collection rate at each location on the sensor such that the atomic population difference equals that of a highly precise optical cavity measurement. This 192 variable regression results in a measurement variance 27% smaller than our previous single variable regression calibration. The measurement variance is now in agreement with the theoretical limit due to other known noise sources. This model efficiently trains in less than a minute on a standard personal computer's CPU and requires less than 10 min of data collection. Furthermore, the model is applicable across a large change in population difference and across data collected on different days.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Material Removal and Surface Integrity Analysis of Ti6Al4V Alloy after Polishing by Flexible Tools with Different Rigidity

Ti6Al4V alloy has been widely used in many fields, such as aerospace and medicine, due to its excellent biocompatibility and mechanical properties. Most high-performance components made of Ti6Al4V alloy usually need to be polished to produce their specific functional requirements. However, due to the material properties of Ti6Al4V, its polishing process still requires significant development. Therefore, this study aimed to investigate the performance of polishing Ti6Al4V by using tools with different rigidities. Two kinds of bonnet tool were used, namely a pure rubber (PR) bonnet and a semirigid (SR) bonnet. The characterization of material removal and surface integrity after polishing was conducted through a series of experiments on a 6-DOF robotic polishing device. The results demonstrate that both bonnet tools successfully produce nanometric level surface roughness. Moreover, the material removal rate of the SR bonnet tool is significantly higher than that of the PR bonnet, which is consistent with the material removal characteristics of glass polishing in previous research. In addition, the presented analysis on key polishing parameters and surface integrity lays the theoretical foundation for the polishing process of titanium alloy in different application fields.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Effect of the Binder during Ultra-Precision Polishing of Tungsten Carbide Using a Semirigid Bonnet Tool

Tungsten carbide (WC) has the characteristics of high hardness, high strength, corrosion resistance, wear resistance and excellent fracture toughness. Accordingly, it has been commonly used as the material for cutting tools and molds in glass-forming techniques. To obtain ultra-smooth surfaces, fine polishing of WC is indispensable. However, the efficiency of WC polishing is low using the existing polishing methods, and the mechanism behind the polishing process requires further investigation. Specifically, the effect of the binder in WC polishing is not clear since there are different kinds of WC with various weight percentages of the binder. In this paper, we present the findings of a study on the polishing performance of two kinds of WC material, with and without the binder, using a semi-rigid (SR) bonnet polishing tool. A series of experiments were performed on a 6-DOF robotic polishing instrument to investigate the material-removal characteristics, surface integrity and sub-surface damage after polishing. The results demonstrate that the SR bonnet polishing tool successfully reduced the surface roughness of WC with and without the binder to the nanometric level, though the lowest surface roughness was obtained on binder-less WC. No obvious sub-surface damage was observed under SEM inspection, while the processing efficiency was greatly improved owing to the high material removal rate of the tool. Based on our analysis of key polishing parameters and corresponding surface integrities, the effect of the binder on the polishing performance is explained, which offers excellent guidance for WC polishing.

ultra-precision machining↗
DOE OSTI2026

Final Technical Report

Explore the source record for details and available documents.

Alsem, Daan Hein [Hummingbird Precision Machine Co↗
DOE OSTI2024

Precision Plant Biomass Characterization in Agriculture: Harnessing Machine Learning and Hyperspectral Imaging [Slides]

Efficient Biomass Separation Object detection of anatomical parts (Cob, Stalk, Husk) in IR images enables precise separation, improving preprocessing (e.g., drying, grinding) for biofuel production. Detailed Biomass Characterization with Hyperspectral Data Hyperspectral imaging captures spectral signatures of biomass, allowing for the identification of specific traits like moisture content, lignin levels, and nutrient composition, leading to optimized treatments for each biomass part. Enhanced Feedstock Quality By leveraging hyperspectral data, feedstock can be processed based on its chemical composition, improving conversion efficiency and biofuel yield. Automation for Large-Scale Operations Automated object detection and hyperspectral data analysis reduce manual labor, ensuring accurate sorting and faster processing, making large-scale biofuel production more efficient. Maximized Biomass Utilization Accurate identification of biomass properties minimizes waste and ensures that each part is processed according to its highest biofuel potential.

09 BIOMASS FUELS↗
DOE OSTI2024

Comparison of Real-Time Pressure Rail Selection Algorithms for the Hybrid Hydraulic Electric Architecture: Case Study on a Track Loader

Abstract The hybrid hydraulic electric architecture (HHEA) seeks to combine the high power/torque/force density of hydraulics with the efficiency of electric machines. A set of common pressure rails is used to provide a majority of the power and this power is modulated by small electric machines to provide precise control for the operator. The HHEA has been studied in previous work using off-line dynamic programming optimization to determine energy efficient pressure rail selections, but this approach requires drive cycle information apriori. A Lagrange multiplier method has also been investigated where a set of gains (Lagrange multipliers) are optimized off-line with the idea the these gains, once determined, could be used for real-time operation. In this work, three new real-time pressure rail selection algorithms that do not require future drive cycle information are investigated; greedy, torque minimizing, and thresholding. The greedy control is found to only use 1% more energy than the globally optimal dynamic programming solution; but a model of energy loss is required.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Displacement driven balancing of a diamond turning machine flycutter

The spindle balancing operation is critical to the performance of precision lathes and should be conducted anytime the configuration of the spindle or its tooling is modified such that an unbalanced condition may occur. Within the machine tool community, accelerometers are typically used as the feedback device during the balancing operation. At Lawrence Livermore National Laboratory (LLNL), we have nearly 40 years of history using displacement sensors, such as capacitance gauges, in place of accelerometers when balancing precision spindles. The process of balancing a precision machine spindle using displacement sensors is presented in the context of two work examples. In this case, the details of the one-dimensional balancing operation performed after replacing a flycutter on the spindles of two unique large optic diamond turning machines is discussed. The pros and cons of accelerometer driven balancing and displacement driven balancing are also presented. In the accelerometer context, the level of balance achieved for our work example is less than 100 micro-g or a total indicator reading (TIR) of 1.6 µm. The process of balancing has been enhanced over the years as advances have been made in software, data acquisition systems, and instrumentation. The balancing system is described including details about instrumentation, set-up, how phase and spindle structure stiffness is calculated, and how the LLNL developed software that guides the user through the process by suggesting corrective action.

42 ENGINEERING↗
DOE OSTI2024

Wasatch Fault Structure from Machine Learning Arrival Times and High-Precision Earthquake Locations

Abstract On 18 March 2020, a magnitude 5.7 earthquake hit the Salt Lake valley in the state of Utah, United States. Using a dense geophone deployment and machine learning (ML), an additional several thousand events were detected and located. Currently, both the mainshock and the majority of the aftershocks are suspected to have occurred on or near a deeper portion of the Salt Lake segment of the Wasatch fault—part of a large range-bounding fault system thought to be capable of generating an Mw 7.2 earthquake. However, a small subset of aftershocks may have occurred on a portion of the more steeply, eastward dipping, and poorly understood West Valley fault. Unfortunately, the catalog locations and lack of focal mechanisms for this subset of aftershocks provide only a crude constraint on the true fault structure. To better illuminate fault structure, we relocate the ML-generated catalog with a range of magnitudes from −2 to 4.6, using: (1) NonLinLoc, a nonlinear location algorithm, (2) source-specific station terms, and (3) waveform coherence. We further compute first-motion focal mechanisms for 68 events. Results of the relocation suggest a simpler, minimally listric Wasatch fault geometry, contrary to what has been previously proposed. We also find that analysis of the focal mechanisms and waveform similarity indicates minimal event similarity throughout the Magna sequence, suggesting a highly complex and heterogeneous rupture zone, as opposed to rupture on a single plane. These findings suggest an increased seismic hazard due to the overall shallowness of the earthquake sequence and highly varied rupture mechanisms.

Geochemistry & Geophysics↗
DOE OSTI2024

Towards Precision Photometric Type ia Supernova Cosmology With Machine Learning

The revolutionary discovery of dark energy and accelerating cosmic expansion was made with just 42 type Ia supernovae (SNe Ia) in 1999. Since then, large synoptic surveys, e.g., Dark Energy Survey (DES), have observed thousands more SNe Ia and the upcoming Rubin Legacy Survey of Space and Time (LSST) and Roman Space Telescope promise to deliver millions in the next decade. This unprecedented data volume can produce the required precision to unambiguously test concordance cosmology which could represent a monumental shift in our understanding of dark energy and its role in cosmic history. However, extracting a pure SN Ia sample with accurate redshifts for such a large dataset will be a challenge. Specifically, spectroscopic classification will not be possible for the vast majority of discovered objects, and only ~25% will have spectroscopic redshifts. This thesis presents a series of observational and methodological studies designed to address the questions associated with this new era of photometric SN Ia cosmology. First, we present a machine learning method for photometric classification of SNe, Supernova Classification with a COnvolutional Neural Network (SCONE). Photometric classification enables SNe with no spectroscopic information to be confidently categorized, and is a critical component of current and future analysis pipelines. SCONE achieves >99% accuracy distinguishing simulated SNe Ia from non-Ia SNe, and has been integrated into DES, LSST, and Roman analysis pipelines. We also demonstrate the efficacy of SCONE on early-time photometric classification, which will be vital for optimal allocation of spectroscopic resources. We show that SCONE can distinguish between 6 SN types with 75% accuracy on the night of initial discovery, comparable to results in the literature for full-phase SNe. Next, we study current methods for estimating SN Ia redshifts and propose a machine learning alternative that uses SN photometry alone to extract redshift information. Most SNe Ia inherit redshift information from their host galaxy, but the process of matching SNe to the correct host galaxy can be challenging. We systematically analyze the impact of incorrect redshifts from host galaxy mismatch on 5 years of DES SN data, and conclude that improved host matching or redshift estimation methods can reduce our systematic errors by ~10%. In response to this finding, we present a SN photometry-only method for estimating redshifts independent of host galaxy information, Photo-zSNthesis. We show that Photo-zSNthesis redshift estimates are accurate to within 2% across the full redshift range of LSST, a first in the literature. Finally, we focus on the robustness of machine learning (ML) algorithms for real-world and scientific applications. ML models generalize poorly beyond their training set and often experience severe performance degradation when deployed on new data. We demonstrate a general method for improving robustness that achieves new state-of-the-art results on astronomical object classification, wildlife identification, and tumor detection.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Servomotor-driven leveling elements for machine tool frame-to-floor alignment

This paper presents the results of aligning the frame to concrete floor of a large 5-axis machine tool by leveling element adjustments using servomotors instead of manual operation. The machine tool leveling elements used in this study are mechanical devices that attach the machine tool frame to the concrete foundation. These leveling elements are also adjustable so that each location along the machine tool can be uniquely adjusted such that the geometric errors of the frame are minimized, and the accuracy of the machine tool is improved. Off-the-shelf servomotors and connection equipment were installed at each of 12 leveling element locations for a precision 5-axis machine tool at Oak Ridge National Laboratory. The servomotors allow a technician to align the machine tool frame remotely, instead of crawling under the machine tool to iteratively manually adjust each of the 12 leveling elements. The open-loop servomotor-leveling element system design is presented in this paper as well as machine tool frame alignment results. The servomotor system significantly reduces leveling element adjustment time, eliminates the need for under-machine access, reduces technician physical strain, and significantly improves the adjustment repeatability.

Honeycutt, Andrew [ORNL]↗
DOE OSTI2023

Data-driven distillation and precision prognosis in traumatic brain injury with interpretable machine learning

Traumatic brain injury (TBI) affects how the brain functions in the short and long term. Resulting patient outcomes across physical, cognitive, and psychological domains are complex and often difficult to predict. Major challenges to developing personalized treatment for TBI include distilling large quantities of complex data and increasing the precision with which patient outcome prediction (prognoses) can be rendered. We developed and applied interpretable machine learning methods to TBI patient data. We show that complex data describing TBI patients' intake characteristics and outcome phenotypes can be distilled to smaller sets of clinically interpretable latent factors. We demonstrate that 19 clusters of TBI outcomes can be predicted from intake data, a ~ 6× improvement in precision over clinical standards. Finally, we show that 36% of the outcome variance across patients can be predicted. These results demonstrate the importance of interpretable machine learning applied to deeply characterized patients for data-driven distillation and precision prognosis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Machine Straightness Error Measurement Based on Optical Fiber Fabry–Pérot Interferometer Monitoring Technique

Abstract In this research, we propose an Error Separation Technique (EST) based on optical fiber sensors for on-machine straightness error measurement. Two fiber optic Fabry–Pérot interferometers have been developed serving as two displacement sensors. The displacement distance is computed according to the reflected spectrum from interferometers, which can achieve a sub-micrometer resolution. The two-point method has been employed to separate the straightness error of the slides and the profile error of a fine-polished standard block. The spacing distance between two interferometers is determined by the diameter of optical fibers so that the EST's resolution has the potential to reach the sub-millimeter scale. In the experiment, the straightness error has been measured on a commercially available computer numerical control machine tool, and the measurement has been conducted on its x-axis. The spacing distance between two optical fiber sensors is 1.5 mm which equals the EST's resolution along the machine tool's x-axis. The separated profile error of the measured standard block is around 30 µm which has been verified by a high precision Coordinate Measurement Machine (CMM). The magnitude of the separated straightness error is around 40 µm. This technique is flexible and simple to be conducted, which can contribute to the micro-machine tool calibration and other straightness error applications.

Engineering↗
DOE OSTI2021

Mixed Precision Fermi-Operator Expansion on Tensor Cores from a Machine Learning Perspective

Here we present a second-order recursive Fermi-operator expansion scheme using mixed precision floating point operations to perform electronic structure calculations using tensor core units. A performance of over 100 teraFLOPs is achieved for half-precision floating point operations on Nvidia’s A100 tensor core units. The second-order recursive Fermi-operator scheme is formulated in terms of a generalized, differentiable deep neural network structure, which solves the quantum mechanical electronic structure problem. We demonstrate how this network can be accelerated by optimizing the weight and bias values to substantially reduce the number of layers required for convergence. We also show how this machine learning approach can be used to optimize the coefficients of the recursive Fermi-operator expansion to accurately represent the fractional occupation numbers of the electronic states at finite temperatures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗