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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Heterogeneity effects on nondestructive assay measurements of enrichment in UF 6 cylinders

A set of measurements were performed using multiple mechanically cooled HPGe detectors at six positions around a cylinder uranium hexafluoride to assess if matrix inhomogeneity is detectible and effects the determined apparent uranium enrichment. These measurements were performed on nine 30B cylinders of uranium hexafluoride. Six of the cylinders appeared to be homogeneous and had similar apparent measured uranium enrichment, as calculated by the FRAM software, at all positions. However, three cylinders appeared to have local inhomogeneity based on the observed results. This was manifested as very low apparent enrichments, often <10% of the declared enrichment, on select positions. The side with a low apparent enrichment shows elevated daughter isotope activity (e.g., elevated 1001 keV and 766 keV peak count rate) and slightly reduced 235 U activity (lower 185.7 keV count rate). We hypothesize that these heterogenous cylinders may have experienced asymmetric solar heating, which caused volatile UF 6 to sublime preferentially away from the warmed side. A similar phenomenon was observed at a second facility where dose rate measurements confirm that the “low-enriched side” of cylinders show elevated gamma ray dose rates, likely from the removal of UF 6 attenuation and concentration of daughter-products. Care should be taken during uranium enrichment verification when applying methods that include gamma-rays associated with daughter nuclides to the determination of uranium enrichment on cylinders that are stored in sunlight and have asymmetric dose readings around the cylinder.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Distributed non-negative matrix factorization with determination of the number of latent features

The holistic analysis and understanding of the latent (that is, not directly observable) variables and patterns buried in large datasets is crucial for data-driven science, decision making and emergency response. Such exploratory analyses require devising unsupervised learning methods for data mining and extraction of the latent features, and non-negative matrix factorization (NMF) is one of the prominent such methods. NMF is based on compute-intense non-convex constrained minimization, which, for large datasets requires fast and distributed algorithms. However, current parallel implementations of NMF fail to estimate the number of latent features. In practice, identifying these features is both difficult and significant for pattern recognition and latent feature analysis, especially for large dense matrices. Here, we introduce a distributed NMF algorithm coupled with distributed custom clustering followed by a stability analysis on dense data, which we call DnMFk, to determine the number of latent variables. The results on synthetic data and the classical Swimmer data set demonstrate the accuracy of model determination while scaling nearly linearly across multiple processors for large data. Further, we employ DnMFk to determine the number of hidden features from a terabyte matrix.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Toughening materials: enhancing resistance to fracture

It has been said that ‘God invented plasticity, but the Devil invented fracture!’ Both mechanisms represent the two prime modes of structural failure, respectively, plastic collapse and the rupture/breaking of a component, but the concept of developing materials with enhanced resistance to fracture can be difficult. This is because fracture resistance invariably involves a compromise—between strength and ductility, between strength and toughness—fundamentally leading to a ‘conflict’ between nano-/micro-structural damage and the mechanisms of toughening. Here, we examine the two major classes of such toughening: (i) intrinsic toughening, which occurs ahead of a crack tip and is motivated by plasticity—this is the principal mode of fracture resistance in ductile materials, and (ii) extrinsic toughening, which occurs at, or in the wake of, a crack tip and is associated with crack-tip shielding—this is generally the sole mode of fracture resistance in brittle materials. We briefly examine how these distinct mechanistic processes have been used to toughen synthetic materials—intrinsically in gradient materials and in multiple principal-element metallic alloys with the example of metallic glasses and high-entropy alloys, and extrinsically in ceramics with the example of ceramic-matrix composites—in comparison to Nature which has been especially adept in creating biological/natural materials which are toughened by one or both mechanistic classes, despite often consisting of constituents with meagre mechanical properties. The success of Nature has been driven by its ability to cultivate the development of materials with multiple length-scale hierarchical structures that display ingenious gradients and structural adaptability, a philosophy which we need to emulate and more importantly learn to synthesize to make structural materials of the future with unprecedented combinations of mechanical properties.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Determining the ballistic threshold velocity for a composite structure using multiple failure models

A composite structure, such as a laminated composite panel, for example, comprises one or more layers or “plies” embedded in a matrix material or otherwise fixed together in an arrangement, commonly referred to as a “stack up.” Each material in the structure has a corresponding material failure model (MFM) defining the physical characteristics of that material. A ballistic threshold velocity computing device obtains the MFMs for each material in the composite structure, generates a predicted ballistic velocity threshold velocity for each MFM, and then generates a parametric model to compute a composite ballistic velocity threshold velocity for the composite structure.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Exploring the energy landscape of RBMs: reciprocal space insights into bosons, hierarchical learning and symmetry breaking

Deep generative models have become ubiquitous due to their ability to learn and sample from complex distributions. Despite the proliferation of various frameworks, the relationships among these models remain largely unexplored, a gap that hinders the development of a unified theory of AI learning. In this work, we address two central challenges: clarifying the connections between different deep generative models and deepening our understanding of their learning mechanisms. We focus on Restricted Boltzmann Machines (RBMs), a class of generative models known for their universal approximation capabilities for discrete distributions. By introducing a reciprocal space formulation for RBMs, we reveal a connection between these models, diffusion processes, and systems of coupled bosons. Our analysis shows that at initialization, the RBM operates at a saddle point, where the local curvature is determined by the singular values of the weight matrix, whose distribution follows the Marc̆enko-Pastur law and exhibits rotational symmetry. During training, this rotational symmetry is broken due to hierarchical learning, where different degrees of freedom progressively capture features at multiple levels of abstraction. This leads to a symmetry breaking in the energy landscape, reminiscent of Landau’s theory. This symmetry breaking in the energy landscape is characterized by the singular values and the weight matrix eigenvector matrix. We derive the corresponding free energy in a mean-field approximation. We show that in the limit of infinite size RBM, the reciprocal variables are Gaussian distributed. Our findings indicate that in this regime, there will be some modes for which the diffusion process will not converge to the Boltzmann distribution. To illustrate our results, we trained replicas of RBMs with different hidden layer sizes using the MNIST dataset. Our findings not only bridge the gap between disparate generative frameworks but also shed light on the fundamental processes underpinning learning in deep generative models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Nonlinear Matrix Approximation with Radial Basis Function Components

We introduce and investigate matrix approximation by decomposition into a sum of radial basis function (RBF) components. An RBF component is a generalization of the outer product between a pair of vectors, where an RBF function replaces the scalar multiplication between individual vector elements. Even though the RBF functions are positive definite, the summation across components is not restricted to convex combinations and allows us to compute the decomposition for any real matrix that is not necessarily symmetric or positive definite. We formulate the problem of seeking such a decomposition as an optimization problem with a nonlinear and non-convex loss function. Several modern versions of the gradient descent method, including their scalable stochastic counterparts, are used to solve this problem. We provide extensive empirical evidence of the effectiveness of the RBF decomposition and that of the gradient-based fitting algorithm. While being conceptually motivated by singular value decomposition (SVD), our proposed nonlinear counterpart outperforms SVD by drastically reducing the memory required to approximate a data matrix with the same L2 error for a wide range of matrix types. For example, it leads to 2 to 6 times memory save for Gaussian noise, graph adjacency matrices, and kernel matrices. Moreover, this proximity-based decomposition can offer additional interpretability in applications that involve, e.g., capturing the inner low-dimensional structure of the data, retaining graph connectivity structure, and preserving the acutance of images.

Rebrova, Elizaveta↗
DOE OSTI2020

Exploration of Electrochemical Reactions at Organic–Inorganic Halide Perovskite Interfaces via Machine Learning in In Situ Time-of-Flight Secondary Ion Mass Spectrometry

The instability of hybrid organic–inorganic perovskite (HOIP) devices is one of the significant challenges preventing commercialization. Exploring these phenomena is severely limited by the complexity of the intrinsic electrochemistry of HOIPs, the presence of multiple volatile and mobile ionic species, and the possible role of environmentally induced reactions at surfaces and triple-phase junctions. Here, in situ studies of the electrochemistry of methylammonium lead bromide perovskite with the Au electrode interface are reported via light- and voltage-dependent time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging of lateral perovskite heterostructures. While ToF-SIMS allows for the visualization of the chemical composition along the surface and its evolution with light and electrical bias, the interpretation of the multidimensional data obtained is often limited due to strong correlations between chemical signatures and the need to track multiple peaks at once. Here, a machine learning workflow combining the Hough transform and non-negative matrix factorization and no-negative tensor decomposition is developed to avoid this limitation and extract salient features of associated chemical changes and to separate the light- and voltage-dependent dynamics. Finally, combining these in situ characterizations and the machine learning workflow provides comprehensive information on the chemical nature of moving species, ion accumulation, and interfacial electrochemical reactions in HOIP devices.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Ion Transport in Self-Assembled Peptoid Membranes with Carbon Nanotube Porin Channels

Artificial membranes that combine high ionic selectivity with mechanical robustness remain a key challenge for next-generation separation technologies. Here, we report ion transport measurements in biomimetic membranes composed of crystalline peptoid nanosheets co-assembled with carbon nanotube porins (CNTPs). Pure peptoid sheets formed defect-free, ion-impermeable membranes, which were then suspended over small SiN x nanopore apertures for ion transport measurements. Incorporation of CNTPs into the peptoid sheet matrix made these membranes ion permeable, with ion conductance values consistent with ion transport through individual and multiple carbon nanotube channels. In conclusion, the modularity and molecular order of peptoid membranes, combined with the exceptional conductance properties of CNTPs, position this platform as a versatile framework for assembling programmable, selective, and robust nanofluidic membranes that can bridge the performance gap between biological and synthetic membrane materials.

Biotechnology↗
DOE OSTI2020

Magnetic properties of γ-Fe 2 O 3 nanoparticles in a porous SiO 2 shell for drug delivery

A method is presented for synthesizing core–shell nanoparticles with a magnetic core and a porous shell suitable for drug delivery and other medical applications. The core contains multiple γ -Fe 2 O 3 nanoparticles (~15 nm) enclosed in a SiO 2 (~100–200 nm) matrix using either methyl (denoted TMOS- γ -Fe 2 O 3 ) or ethyl (TEOS- γ -Fe 2 O 3 ) template groups. Low-temperature Mössbauer spectroscopy showed that the magnetic nanoparticles have the maghemite structure, γ -Fe 2 O 3 , with all the vacancies in the octahedral sites. Saturation magnetization measurements revealed that the density of γ -Fe 2 O 3 was greater in the TMOS- γ -Fe 2 O 3 nanoparticles than TEOS- γ -Fe 2 O 3 nanoparticles, presumably because of the smaller methyl group. Magnetization measurements showed that the blocking temperature is around room temperature for the TMOS- γ -Fe 2 O 3 and around 250 K for the TEOS- γ -Fe 2 O 3 . Three dimensional topography analysis shows clearly that the magnetic nanoparticles are not only at the surface but have penetrated deep in the silica to form the core–shell structure.

Physics↗
DOE OSTI2025

Detection of Br and I as atomic anions using liquid sampling – atmospheric pressure glow discharge/Orbitrap mass spectrometry

The quantification and determination of halogen isotope composition is essential in many fields including geochemistry and nuclear forensics. Detection of the halogen elements is commonly attempted with inductively coupled plasma mass spectrometry (ICP-MS) however, there are multiple challenges faced. Most significantly this includes very poor ionization efficiency and the potential for isobaric interferences from either matrix or plasma species. Thus, sensitivity and accuracy are often limiting issues. Here a liquid sampling-atmospheric pressure glow discharge (LS-APGD) ionization source is coupled with an ultrahigh resolution Orbitrap MS to perform halogen detection of bromine and iodine as initial analytes. This facilitates simple and sensitive detection of these elements as atomic anions from simple aqueous salt solutions. Collision-induced dissociation (CID) and higher-energy collisional dissociation (HCD) energies were optimized to produce the maximum response of Br − and I − . The LS-APGD conditions were optimized using a design of experiments (DOE) approach for Br − and I − concurrently. Response curves for Br − and I − solutions determined limit of detection (LOD) values of 50 pg and 5 pg, respectively, in 20 μL aliquots. The curves indicated response factors of 0.67 for 79 Br − and 0.90 for 127 I − . Br isotope ratios were determined with precision between 0.7 and 7.3% RSD, with the isotope ratios determined with precision 0.8% RSD at concentrations above the limit of quantification. Preliminary tests were conducted to evaluate the effect of different cations (Na + , K + , and Mg 2+ ) on Br − responses, with no discernible impacts observed on the halogen signal responses. This study demonstrates the potential use of the LS-APGD-Orbitrap-MS for the detection of multiple halogens while avoiding interferences, minimizing sample preparation, and overcoming ionization barriers.

Lang, Dehlia A. [Clemson University, SC (United St↗
DOE OSTI2025

AAPM Truth‐based CT (TrueCT) reconstruction grand challenge

Background: This Special Report summarizes the 2022, AAPM grand challenge on Truth-based CT image reconstruction. Purpose: To provide an objective framework for evaluating CT reconstruction methods using virtual imaging resources consisting of a library of simulated CT projection images of a population of human models with various diseases. Methods: Two hundred unique anthropomorphic, computational models were created with varied diseases consisting of 67 emphysema, 67 lung lesions, and 66 liver lesions. The organs were modeled based on clinical CT images of real patients. The emphysematous regions were modeled using segmentations from patient CT cases in the COPDGene Phase I dataset. For the lung and liver lesion cases, 1–6 malignant lesions were created and inserted into the human models, with lesion diameters ranging from 5.6 to 21.9 mm for lung lesions and 3.9 to 14.9 mm for liver lesions. The contrast defined between the liver lesions and liver parenchyma was 82 ± 12 HU, ranging from 50 to 110 HU. Similarly, the contrast between the lung lesions and the lung parenchyma was defined as 781 ± 11 HU, ranging from 725 to 805 HU. For the emphysematous regions, the defined HU values were −950 ± 17 HU ranging from −918 to −979 HU. The developed human models were imaged with a validated CT simulator. The resulting CT sinograms were shared with the participants. The participants reconstructed CT images from the sinograms and sent back their reconstructed images. Further, the reconstructed images were then scored by comparing the results against the corresponding ground truth values. The scores included both task-generic (root mean square error [RMSE] and structural similarity matrix [SSIM]), and task-specific (detectability index [d’] and lesion volume accuracy) metrics. For the cases with multiple lesions, the measured metric was averaged across all the lesions. To combine the metrics with each other, each metric was normalized to a range of 0 to 1 per disease type, with “0” and “1” being the worst and best measured values across all cases of the disease type for all received reconstructions. Results: The True-CT challenge attracted 52 participants, out of which 5 successfully completed the challenge and submitted the requested 200 reconstructions. Across all participants and disease types, SSIM absolute values ranged from 0.22 to 0.90, RMSE from 77.6 to 490.5 HU, d’ from 0.1 to 64.6, and volume accuracy ranged from 1.2 to 753.1 mm3. The overall scores demonstrated that participant “A” had the best performance in all categories, except for the metrics of d’ for lung lesions and RMSE for liver lesions. Participant “A” had an average normalized score of 0.41 ± 0.22, 0.48 ± 0.32, and 0.42 ± 0.33 for the emphysema, lung lesion, and liver lesion cases, respectively. Conclusions: The True-CT challenge successfully enabled objective assessment of CT reconstructions with the unique advantage of access to a diverse population of diseased human models with known ground truth. This study highlights the significant potential of virtual imaging trials in objective assessment of medical imaging technologies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Transmission Electron Microscopy based Characterization of a U-20Pu-10Zr Fuel Irradiated in Experimental Breeder Reactor-II

U-Pu-Zr metallic fuels are important fuel candidate for future advanced and/or test reactors. To better understand fuel performance and guide future fuel development, a U-20Pu-10Zr (in weight) metallic fuel irradiated in the Experimental Breeder Reactor was revisited using advanced electron microscopies. This fuel was irradiated to a burnup of 7.6 % at a peak cladding temperature of 521 °C. In this research, several transmission electron microscopy samples were extracted from different but representative radial locations of the fuel cross section using focused ion beam technique. Phase identification and chemical analysis with sub-micron spatial resolution was carried out using scanning transmission electron microscopy. Multiple phenomena that are critical to fuel performance, such as Zr phase and its distribution in a different matrix, were revealed in an unpreceded and highly detailed manner. An improved understanding of fuel reconstruction in U-Pu-Zr metallic fuel under reactor irradiation is provided.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Improving inference with matrix elements and machine learning

Particle physics processes bring together a high-energy amplitude described by quantum field theory and nonperturbative effects and detector interactions described by complex computer simulations. We review some recently developed multivariate inference techniques that leverage this structure and combine matrix-element information with machine learning. Automated by the MadMiner package, the new techniques have been applied to multiple problems in particle physics, allowing for stronger limits than traditional analysis methods and showing their potential to improve the sensitivity of the LHC legacy measurements.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Structural, Thermal, and Mechanical Characterization of a Thermally Conductive Polymer Composite for Heat Exchanger Applications

Polymer composites are being considered for numerous thermal applications because of their inherent benefits, such as light weight, corrosion resistance, and reduced cost. In this work, the microstructural, thermal, and mechanical properties of a 3D printed polymer composite with high thermal conductivity are examined using multiple characterization techniques. Infrared spectroscopy and X-ray diffraction reveal that the composite contains a polyphenylene sulfide matrix with graphitic fillers, which is responsible for the high thermal conductivity. Furthermore, differential scanning calorimetry determines that the glass transition and melting point of the composite are 87.6 °C and 285.6 °C, respectively. Thermogravimetric analysis reveals that the composite is thermally stable up to ~400 °C. Creep tests are performed at different isotherms to evaluate the long-term performance of the composite. The creep result indicates that the composite can maintain mechanical integrity when used below its glass transition temperature. Nanoindentation tests reveal that modulus and hardness of the composite is not significantly influenced by heating or creep conditions. These findings indicate that the composite is potentially suitable for heat exchanger applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Why Is Attention Sparse In Particle Transformer?

Transformer-based models have achieved state-of-the-art performance in jet tagging at the CERN Large Hadron Collider (LHC), with the Particle Transformer (ParT) representing a leading example of such models. A striking feature of ParT is its sparse, nearly binary, attention structure, raising questions about the origin of this behavior and whether it encodes physically meaningful correlations. In this work, we investigate the source of ParT's sparse attention by comparing models trained on multiple benchmark datasets and examine the relative contributions of the attention term and the physics-inspired interaction matrix before softmax. We find that binary sparsity arises primarily from the attention mechanism itself, with the interaction matrix playing a secondary role. Moreove, we show that ParT is able to identify key jet substructure elements, such as leptons in semileptonic top decays, even without explicit particle identification inputs. These results provide new insight into the interpretability of transformer-based jet taggers and clarify the conditions under which sparse attention patterns emerge in ParT.

Legge, Timothy [UC, San Diego]↗
DOE OSTI2021

Effect of the Segmental Structure of Thermoplastic Polyurethane (Hardness) on the Interfacial Adhesion of Textile-Grade Carbon Fiber Composites

In polymer composites, the fiber–matrix interface is primarily influenced by the surface treatment of the fibers and polymer morphology. Previous studies have investigated the effect of surface treatment of carbon fiber on the mechanical properties of the resulting composites. However, very few studies have explored the chemical structural modification of polymer effect on the fiber–matrix adhesion. In this work, the interfaces of soft thermoplastic polyurethane (S-TPU) and hard-segmented TPU (H-TPU) were investigated through surface, thermal, and mechanical characterization. A textile-grade carbon fiber (TCF) with 1% concentration of epoxy sizing (an emerging material for nonaerospace applications with 450 K filament tows) was used as a reinforcement to investigate the structure–property relationship at the interface. Atomic force microscopy results showed 39% higher surface roughness for S-TPU than for H-TPU. X-ray photoelectron spectroscopy results revealed a 550% increase in C=O content, which can provide multiple hydrogen bonding networks in H-TPU, and these C=O bonds can produce a strong chemical bond at the fiber–matrix interface. Additionally, dynamic mechanical analysis and differential scanning calorimetry results confirmed the presence of hydrogen bonding that enhances the cross-linked density by 232% in H-TPU compared to S-TPU. The improved mechanical properties of H-TPU composites, such as flexural, impact and tensile by 30, 50, and 130% compared to S-TPU composites, prove that the crystallinity and hydrogen bonding significantly increase the load bearing capacity due to the strong interface. The mechanical properties of TCF–TPU composites validate the formation of chemical bonds through a nucleophilic addition reaction at the interface and improve the bond strength of the composites. Thus, tailoring the polyurethane structure broadens the performance of segmented TPU in conjunction with TCF reinforcement, which has implications for cost-effective, high-strength composites.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Three dimensional nanoporous membrane having multiple independent, continuous pore systems

According to one embodiment, a composition of matter includes: a first system of continuous voids arranged in a three-dimensional matrix; a second system of continuous voids arranged in the three-dimensional matrix; and a nanoporous barrier separating the first system of continuous voids and the second system of continuous voids. The first system of continuous voids and the second system of continuous voids are interwoven but independent so as to form a plurality of channels through the three-dimensional matrix. Corresponding methods for forming the composition of matter are also disclosed.

Ye, Jianchao↗
DOE OSTI2022

Improving convergence of the Matrix Power Control Algorithm for random vibration testing

Herein, this paper describes modifications to the Matrix Power Control Algorithm (MPCA) to improve convergence for Random Vibration Control (RVC) testing. In particular, this paper presents Multiple-Input Multiple-Output (MIMO) implementations of MPCA in simulation and experiment. An Euler–Bernoulli beam model was simulated with applied base excitations and the Box Assembly with Removable Component (BARC) was used in experiment to validate results. The Bayesian optimization package Dragonfly was used to optimize control parameters. Additionally, a moving-average was employed and optimized to improve the measured response feedback for MPCA, reduce the number of averages needed to be taken between control updates, and further improve convergence. The key results of this paper show that the performance of MPCA can be improved by tuning the control parameters and by applying an optimized moving-average. Furthermore, it is demonstrated that convergence can be achieved within 12 drive-frames, which greatly enhances vibration control capability.

42 ENGINEERING↗