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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

Enhanced joining strength in additive-manufactured polylactic-acid structures fused by embedded heated metallic meshes

Additively manufactured thermoplastic polymers, such as polylactic acid (PLA), hold significant promise for sustainable engineering structures, including wind turbine blades. Upscaling these structures beyond the limitations of 3D printer build volumes is a challenge; fusion joining presents a potential solution. This paper introduces a displacement-controlled resistance welding process for PLA, as an alternative to the typical force-controlled methods. Here, we investigated the bonding quality of resistance-welded and adhesive-bonded PLA beams through three-point bending and measured the surface deformations using digital image correlation. Different metal meshes (30%/0.11 mm Ni—Cu, 34%/0.07 mm Ni—Cu, and 36%/0.25 mm Co—Ni) served as heating elements. The process parameters were varied for the 34%/0.07 mm Ni—Cu mesh to identify an optimum set of parameters. Results showed that this optimized displacement-controlled welding achieved 94% of the original strength of monolithic samples. This indicates that the new welding process not only ensures high-quality bonding and fine surface finishing but also promotes sustainability, recyclability, and economic efficiency in various polymer and composite structural applications.

Additive manufacturing↗
DOE OSTI2025

Machine Learning-Based Process Control for Injection Molding of Recycled Polypropylene

The increased interest in artificial intelligence in manufacturing has driven the adoption of machine learning to optimize processes and improve efficiency. A key challenge in injection molding is the variability of recycled materials, which affects part quality and processing stability. This study presents a novel closed-loop process control approach for injection molding, leveraging machine learning to adaptively predict processing inputs and quality outcomes. The methodology was tested on five blends of recycled polypropylene (rPP), using artificial neural networks (ANNs), linear regression, and polynomial regression to model the relationships between material properties and process parameters. The dataset was split 80/20 into training and testing sets. The ANN model was implemented using TensorFlow and Keras, with six hidden layers of 32 neurons per layer, ReLU activation, and an Adam optimizer. Empirical tuning and early stopping were used to optimize performance and prevent overfitting. Predictions were evaluated based on mean absolute error (MAE), mean squared error (MSE), and percentage error. The results showed that yield stress, ultimate elongation, and part weight were accurately predicted within a 5% error for linear and polynomial regression models and within a 10% error for the ANN. However, modulus predictions were less reliable, with errors of ~11% for ANN and linear regression and ~40% for polynomial regression, reflecting the inherent variability of this property in rPP blends. Predictions of processing inputs had errors ranging from 3% to 25%, depending on the model and response variable. No single modeling approach was consistently superior across all responses, highlighting the complexity of the relationship between material properties, process parameters, and quality metrics. Overall, the work demonstrates that closed-loop process control, powered by machine learning, can effectively predict key quality parameters in injection molding of recycled materials. The proposed approach can improve process stability and material utilization, facilitating increased adoption of sustainable materials.

Krantz, Joshua↗
DOE OSTI2022

The Edge of Exploration: An Edge Storage and Computing Framework for Ambient Noise Seismic Interferometry Using Internet of Things Based Sensor Networks

Recent technological advances have reduced the complexity and cost of developing sensor networks for remote environmental monitoring. However, the challenges of acquiring, transmitting, storing, and processing remote environmental data remain significant. The transmission of large volumes of sensor data to a centralized location (i.e., the cloud) burdens network resources, introduces latency and jitter, and can ultimately impact user experience. Edge computing has emerged as a paradigm in which substantial storage and computing resources are located at the “edge” of the network. In this paper, we present an edge storage and computing framework leveraging commercially available components organized in a tiered architecture and arranged in a hub-and-spoke topology. The framework includes a popular distributed database to support the acquisition, transmission, storage, and processing of Internet-of-Things-based sensor network data in a field setting. We present details regarding the architecture, distributed database, embedded systems, and topology used to implement an edge-based solution. Lastly, a real-world case study (i.e., seismic) is presented that leverages the edge storage and computing framework to acquire, transmit, store, and process millions of samples of data per hour.

58 GEOSCIENCES↗
DOE OSTI2024

The Tiny Median Filter: A Small Size, Flexible Arbitrary Percentile Finder Scheme Suitable for FPGA Implementation

This document reports the design, implementation and testing of a small silicon resource usage, very flexible arbitrary percentile finding scheme called the Tiny Median Filter. It can be used not only as a median filter in image processing with square filtering windows, but also for applications of any percentile filter or maximum or minimum finder with any size of data set as long as the number of bits of the data is finite. It opens possibilities for image processing tasks with non-square or irregular filter windows. In this scheme, data swapping or data bit manipulating are avoided and high functional efficiency of the logic components is applied to save silicon resources. Some logic functions are absorbed into other functions to further reduce the complexity. The combinational logic paths are designed to be sufficiently short so that the firmware can be compiled to the maximum operating frequency allowed by the block memories of the FPGA devices. The Tiny Median Filter receives, processes and output data in non-stop manner with no irregular timing which helps to simplify design of surrounding stages.

Wu, Jinyuan [Fermilab] (ORCID:0000000344329521)↗
DOE OSTI2023

Optimal adjustment sets for causal query estimation in partially observed biomolecular networks

Abstract Causal query estimation in biomolecular networks commonly selects a ‘valid adjustment set’, i.e. a subset of network variables that eliminates the bias of the estimator. A same query may have multiple valid adjustment sets, each with a different variance. When networks are partially observed, current methods use graph-based criteria to find an adjustment set that minimizes asymptotic variance. Unfortunately, many models that share the same graph topology, and therefore same functional dependencies, may differ in the processes that generate the observational data. In these cases, the topology-based criteria fail to distinguish the variances of the adjustment sets. This deficiency can lead to sub-optimal adjustment sets, and to miss-characterization of the effect of the intervention. We propose an approach for deriving ‘optimal adjustment sets’ that takes into account the nature of the data, bias and finite-sample variance of the estimator, and cost. It empirically learns the data generating processes from historical experimental data, and characterizes the properties of the estimators by simulation. We demonstrate the utility of the proposed approach in four biomolecular Case studies with different topologies and different data generation processes. The implementation and reproducible Case studies are at https://github.com/srtaheri/OptimalAdjustmentSet.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Measurement of Quark Energy Loss in Cold Nuclear Matter at Fermilab E906/SeaQuest

Parton energy loss is a process within QCD that draws considerable interest. The measurement of parton energy loss can provide valuable information for other hard-scattering processes in nuclei, and also serves as an important tool for exploring the properties of the quark-gluon plasma (QGP). Quantifying the energy loss in cold nuclear matter will help to set a baseline relative to energy loss in the QGP. With the Drell-Yan process, the energy loss of incoming quarks in cold nuclear matter can be ideally investigated since the final state interaction is expected to be minimal. E906/SeaQuest is a fixed-target experiment using the 120 GeV proton beam from the Fermilab Main Injector and has been collecting data from p+p, p+d, p+C, p+Fe, and p+W collisions. Within the E906 kinematic coverage of Drell-Yan production via the dimuon channel, the quark energy loss can be measured in a regime where other nuclear effects are expected to be small. In this thesis, the study of quark ener gy loss from different cold nuclear targets is presented.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Grey-Box System Identification of Grid-Forming Inverters

This paper demonstrates the use of grey-box system identification methods for simplifying and understanding the nonlinear power dynamics of grid-forming inverters (GFMs). The power and frequency outputs of complex high-order GFM models are fed into system identification software in order to fit them to a predetermined LTI system and learn system parameters such as (synthetic) inertia and droop constants. The same process is then run for a high-order synchronous generator model, and the outputs are fit to the same set of LTI equations. Simulation of a network of GFM inverters with diverse control architecture is also performed for the same process. The intent is threefold: first, to demonstrate the appropriateness of unified LTI models for describing the power and frequency dynamics of individual resources and connected networks, in order to facilitate analysis of larger heterogeneous networked systems; second, to discover the relationship between internal control parameters of GFMs and their externally observed values; and third, to validate that grey-box data-driven system identification techniques can be a valuable tool to discover the values of important parameters in the absence of explicit vendor models.

analytical models↗
DOE OSTI2024

Exploring the potential and impact of single-crystal active materials on dry-processed electrodes for high-performance lithium-ion batteries

Roll-to-roll powder-to-film dry processing (DP) and single-crystal (SC) active materials (AMs) with many advantages are two hot topics of lithium-ion batteries (LIBs). However, DP of SC AMs for LIBs is rarely reported. Consequently, the impact of SC AMs on dry-processed LIBs is not well understood. Herein, for the first time, via a set of experimental and theoretical studies of the conventional polycrystalline-AM- and SC-AM-based DPed electrodes (DPEs), this work not only reports a high-performance dry SC-AM cathode for LIB manufacturing, but also establishes some fundamental understanding of SC-based dry-processed electrodes, including their morphology, structure, mechanical strength, electronic conductivity and LIB electrochemical behavior. Further, the results suggest that DP of SC AMs is promising, which can dramatically improve the electrochemical kinetics at electrode level and particle level. Specifically, for the rate capability and long-term cyclability in full cells, SC DPEs exhibit a discharge specific capacity of 152.1 mAh g -1 at 1C and a capacity retention rate of 79.9 % at C/3 over 500 cycles, which are superior to those of PC DPEs (135.6 mAh g -1 and 68.3 %) at the same conditions and are further confirmed by the simulation data from the theoretical modelling study. Therefore, this comprehensive work marks a significant milestone for DP strategy and SC AMs, enlightening future research and development of LIB manufacturing.

25 ENERGY STORAGE↗
DOE OSTI2016

Reconstruction of the regulatory network for Bacillus subtilis and reconciliation with gene expression data

Here, we introduce a manually constructed and curated regulatory network model that describes the current state of knowledge of transcriptional regulation of B. subtilis . The model corresponds to an updated and enlarged version of the regulatory model of central metabolism originally proposed in 2008. We extended the original network to the whole genome by integration of information from DBTBS, a compendium of regulatory data that includes promoters, transcription factors (TFs), binding sites, motifs and regulated operons. Additionally, we consolidated our network with all the information on regulation included in the SporeWeb and Subtiwiki community-curated resources on B. subtilis . Finally, we reconciled our network with data from RegPrecise, which recently released their own less comprehensive reconstruction of the regulatory network for B. subtilis . Our model describes 275 regulators and their target genes, representing 30 different mechanisms of regulation such as TFs, RNA switches, Riboswitches and small regulatory RNAs. Overall, regulatory information is included in the model for approximately 2500 of the ~4200 genes in B. subtilis 168. In an effort to further expand our knowledge of B. subtilis regulation, we reconciled our model with expression data. For this process, we reconstructed the Atomic Regulons (ARs) for B. subtilis , which are the sets of genes that share the same “ON” and “OFF” gene expression profiles across multiple samples of experimental data. We show how atomic regulons for B. subtilis are able to capture many sets of genes corresponding to regulated operons in our manually curated network. Additionally, we demonstrate how atomic regulons can be used to help expand or validate the knowledge of the regulatory networks by looking at highly correlated genes in the ARs for which regulatory information is lacking. During this process, we were also able to infer novel stimuli for hypothetical genes by exploring the genome expression metadata relating to experimental conditions, gaining insights into novel biology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Vorticity, shocks, and magnetic fields in subsonic, ICM-like turbulence

We analyze data from high resolution simulations of the generation of compressible, MHD turbulence with properties chosen to resemble conditions in galaxy clusters. In particular, the flow is driven to have turbulence Mach number M{sub t}∼1/2 in an isothermal medium with an initially very weak, uniform seed magnetic field (β=P{sub g}/P{sub B}=10{sup 6}). Since cluster turbulence is likely to result from a mix of sheared (solenoidal) and compressive forcing processes, we examine the distinct turbulence properties for both cases. In one set of simulations velocity forcing is entirely solenoidal (∇⋅δu=0), while in the other it is entirely compressive (∇×δu=0). Both cases develop a mixture of solenoidal and compressive turbulent motions, since each generates the other. The development of compressive turbulent motions leads to shocks, even when the turbulence is solenoidally forced and subsonic. Shocks, in turn, produce and amplify vorticity, which is especially important in compressively forced turbulence. To clarify those processes we include a pair of appendices that look in detail at vorticity evolution in association with shocks. From our simulation analyses we find that magnetic fields amplified to near saturation levels in predominantly solenoidal turbulence can actually enhance vorticity on small scales by concentrating and stabilizing shear. The properties, evolution rates, and relative contributions of the kinetic and magnetic turbulent elements depend strongly on the character of the forcing. Specifically, shocks are stronger, but vorticity evolution and magnetic field amplification are slower and weaker when the turbulence is compressively forced. We identify a simple relation to estimate characteristic shock strengths in terms of the turbulence Mach number and the character of the forcing. Our results will be helpful in understanding flow motions in galaxy clusters.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

TENDL: Complete Nuclear Data Library for Innovative Nuclear Science and Technology

The TENDL library is now established as one of the major nuclear data libraries in the world, striving for completeness and quality of nuclear data files for all isotopes, evaluation methods, processing and applied performance. To reach this status, some basic principles have been applied which sets it apart from other libraries: reproducible dedicated evaluations when differential data are available, through determination of nuclear models implemented in TALYS and their parameters, completeness (with or without experimental data), format and processing standardization, automation of production and reproducibility. In this paper, we will outline how such an approach has become a reality, and recall some of the past successes since the first TENDL release in 2008. Next, we will demonstrate the performance of the latest TENDL releases for different application fields, as well as new approaches for uncertainty quantification based on Bayesian inference methods and possible differential and integral adjustments. Also, current limitations of the library performances due to modelling and needs for new and more precise experimental data will be outlined.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2016

Improvement of General Electric’s Chilled Ammonia Process with the use of Membrane Technology

General Electric Environmental Control Solutions (formerly Alstom Power Environmental Control Systems) set out to complete the Phase 1 award requirements for a Phase II renewal application for their project selected under DOE-FOA-0001190 “Small and Large Scale Pilots for Reducing the Cost of CO 2 Capture and Compression”. The project focus was to implement several improvement concepts utilizing membrane technology at the recipient’s Chilled Ammonia Process (CAP) CO 2 capture large-scale pilot plant. The goal was to lower the overall cost of technology. During the development of costs for the preliminary techno-economic assessment (TEA), it became clear that the capital and operating costs of this concept were not economically attractive. All work related to a Phase II renewal application at that point was halted as GE made the decision not to submit a Phase II renewal application. Discussions with DOE resulted in a path towards useful information produced from the design and cost work already completed on the project. With the reverse osmosis (RO) unit providing most of the cost issues, GE would provide a sensitivity analysis of the RO unit with respect to project cost. This information would be included with the Techno-Economic Analysis along with the Technology Gap Analysis.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2025

Fast jet tagging with MLP-Mixers on FPGAs

We explore the innovative use of MLP-Mixer models for real-time jet tagging and establish their feasibility on resource-constrained hardware like FPGAs. MLP-Mixers excel in processing sequences of jet constituents, achieving state-of-the-art performance on datasets mimicking Large Hadron Collider conditions. By using advanced optimization techniques such as High-Granularity Quantization and Distributed Arithmetic, we achieve unprecedented efficiency. These models match or surpass the accuracy of previous architectures, reduce hardware resource usage by up to 97%, double the throughput, and half the latency. Additionally, non-permutation-invariant architectures enable smart feature prioritization and efficient FPGA deployment, setting a new benchmark for machine learning in real-time data processing at particle colliders.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Uncovering Uranium Isotopic Heterogeneity of Fuel Pellets from the Fifth Collaborative Materials Exercise of the Nuclear Forensics International Technical Working Group

In 2017, the Nuclear Forensics International Technical Working Group (ITWG) organized their fifth 37 Collaborative Materials Exercise (CMX-5). The exercise samples were two uranium dioxide fuel pellets 38 manufactured from the same starting materials by different processes to have similar bulk isotopic 39 composition, but different spatial uranium isotopic distributions. Sets of identical materials were sent to 40 all participating laboratories, who then utilized their existing nuclear forensic capabilities to 41 independently analyse fuel pellets and identify similarities and differences of the materials’ 42 characteristics. The analytical methods used to probe the fuel pellets included ex situ, such as sectioning 43 or breaking up the pellets and analyzing dissolved pieces using inductively coupled plasma mass 44 spectrometry (ICP-MS), analyzing particles collected from intact or fragmented pellets by secondary ion 45 mass spectrometry (SIMS), as well as in situ methods, such as laser ablation coupled with ICP-MS, 46 autoradiography and nanoSIMS. In this paper we present the results of these independent analyses and 47 compare the capabilities of those nuclear forensic analytical methods to uncover details of the isotopic 48 heterogeneity of uranium fuel pellets.

Nuclear Forensic Analysis of Uranium Fuel Pellets,↗
DOE OSTI2023

The Alcock–Paczynski effect from Lyman- α forest correlations: analysis validation with synthetic data

The three-dimensional distribution of the Ly α forest has been extensively used to constrain cosmology through measurements of the baryon acoustic oscillations (BAO) scale. However, more cosmological information could be extracted from the full shapes of the Ly α forest correlations through the Alcock–Paczynski (AP) effect. In this work, we prepare for a cosmological analysis of the full shape of the Ly α forest correlations by studying synthetic data of the extended Baryon Oscillation Spectroscopic Survey (eBOSS). We use a set of 100 eBOSS synthetic data sets in order to validate such an analysis. These mocks undergo the same analysis process as the real data. We perform a full-shape analysis on the mean of the correlation functions measured from the 100 eBOSS realizations, and find that our model of the Ly α correlations performs well on current data sets. We show that we are able to obtain an unbiased full-shape measurement of D M /D H (z eff ), where D M is the transverse comoving distance, D H is the Hubble distance, and z eff is the effective redshift of the measurement. We test the fit over a range of scales, and decide to use a minimum separation of r min = 25 h –1 Mpc. Here, we also study and discuss the impact of the main contaminants affecting Ly α forest correlations, and give recommendations on how to perform such analysis with real data. While the final eBOSS Ly α BAO analysis measured D M /D H (z eff = 2.33) with 4 per cent statistical precision, a full-shape fit of the same correlations could provide an $\sim 2~{{\ \rm per\ cent}}$ measurement.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Leveraging design of experiments to build chemometric models for the quantification of uranium (VI) and HNO3 by Raman spectroscopy

Partial least squares regression (PLSR) and support vector regression (SVR) models were optimized for the quantification of U(VI) (10–320 g L −1 ) and HNO 3 (0.6–6 M) by Raman spectroscopy with optimized calibration sets chosen by optimal design of experiments. The designed approach effectively minimized the number of samples in the calibration set for PLSR and SVR by selecting sample concentrations with a quadratic process model, despite complex confounding and covarying spectral features in the spectra. The top PLS2 model resulted in percent root mean square errors of prediction for U(VI), HNO 3 , and NO 3 − of 3.7%, 3.6%, and 2.9%, respectively. PLS1 models performed similarly despite modeling an analyte with a majority linear response (i.e., uranyl symmetric stretch) and another with more covarying vibrational modes (i.e., HNO 3 ). Partial least squares (PLS) model loadings and regression coefficients were evaluated to better understand the relationship between weaker Raman bands and covarying spectral features. Support vector machine models outperformed PLS1 models, resulting in percent root mean square error of prediction values for U(VI) and HNO 3 of 1.5% and 3.1%, respectively. The optimal nonlinear SVR model was trained using a similar number of samples (11) compared with the PLSR model, even though PLS is a linear modeling approach. The generic D-optimal design presented in this work provides a robust statistical framework for selecting training set samples in disparate two-factor systems. This approach reinforces Raman spectroscopy for the quantification of species relevant to the nuclear fuel cycle and provides a robust chemometric modeling approach to bolster online monitoring in challenging process environments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Trident: A Universal Tool for Generating Synthetic Absorption Spectra from Astrophysical Simulations

Hydrodynamical simulations are increasingly able to accurately model physical systems on stellar, galactic, and cosmological scales; however, the utility of these simulations is often limited by our ability to directly compare them with the data sets produced by observers: spectra, photometry, etc. To address this problem, we have created trident, a Python-based open-source tool for post-processing hydrodynamical simulations to produce synthetic absorption spectra and related data. trident can (i) create absorption-line spectra for any trajectory through a simulated data set mimicking both background quasar and down-the-barrel configurations; (ii) reproduce the spectral characteristics of common instruments like the Cosmic Origins Spectrograph; (iii) operate across the ultraviolet, optical, and infrared using customizable absorption-line lists; (iv) trace simulated physical structures directly to spectral features; (v) approximate the presence of ion species absent from the simulation outputs; (vi) generate column density maps for any ion; and (vii) provide support for all major astrophysical hydrodynamical codes. trident was originally developed to aid in the interpretation of observations of the circumgalactic medium and intergalactic medium, but it remains a general tool applicable in other contexts.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2016

SU-F-P-48: The Quantitative Evaluation and Comparison of Image Distortion and Loss of X-Ray Images Between Anti-Scattered Grid and Moire Compensation Processing in Digital Radiography

Purpose: To quantitatively analyze the influence image processing for Moire elimination has in digital radiography by comparing the image acquired from optimized anti-scattered grid only and the image acquired from software processing paired with misaligned low-frequency grid. Methods: Special phantom, which does not create scattered radiation, was used to acquire non-grid reference images and they were acquired without any grids. A set of images was acquired with optimized grid, aligned to pixel of a detector and other set of images was acquired with misaligned low-frequency grid paired with Moire elimination processing algorithm. X-ray technique used was based on consideration to Bucky factor derived from non-grid reference images. For evaluation, we analyze by comparing pixel intensity of acquired images with grids to that of reference images. Results: When compared to image acquired with optimized grid, images acquired with Moire elimination processing algorithm showed 10 to 50% lower mean contrast value of ROI. Severe distortion of images was found with when the object’s thickness was measured at 7 or less pixels. In this case, contrast value measured from images acquired with Moire elimination processing algorithm was under 30% of that taken from reference image. Conclusion: This study shows the potential risk of Moire compensation images in diagnosis. Images acquired with misaligned low-frequency grid results in Moire noise and Moire compensation processing algorithm used to remove this Moire noise actually caused an image distortion. As a result, fractures and/or calcifications which are presented in few pixels only may not be diagnosed properly. In future work, we plan to evaluate the images acquired without grid but based on 100% image processing and the potential risks it possesses.

60 APPLIED LIFE SCIENCES↗