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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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Optically Induced Picosecond Lattice Compression in the Dielectric Component of a Strongly Coupled Ferroelectric/Dielectric Superlattice

Above-bandgap femtosecond optical excitation of a ferroelectric/dielectric BaTiO 3 /CaTiO 3 superlattice leads to structural responses that are a consequence of the screening of the strong electrostatic coupling between the component layers. Time-resolved X-ray free-electron laser diffraction shows that the structural response to optical excitation includes a net lattice expansion of the superlattice consistent with depolarization-field screening driven by the photoexcited charge carriers. The depolarization-field-screening-driven expansion is separate from a photoacoustic pulse launched from the bottom electrode on which the superlattice is epitaxially grown. The distribution of diffracted intensity of superlattice X-ray reflections indicates that the depolarization-field-screening-induced strain includes a photoinduced expansion in the ferroelectric BaTiO 3 and a contraction in CaTiO 3 . The magnitude of expansion in BaTiO 3 layers is larger than the contraction in CaTiO 3 . The difference in the magnitude of depolarization-field-screening-driven strain in the BaTiO 3 and CaTiO 3 components can arise from the contribution of the oxygen octahedral rotation patterns at the BaTiO 3 /CaTiO 3 interfaces to the polarization of CaTiO 3 . The depolarization-field-screening-driven polarization reduction in the CaTiO 3 layers points to a new direction for the manipulation of polarization in the component layers of a strongly coupled ferroelectric/dielectric superlattice.

36 MATERIALS SCIENCE↗
DOE OSTI2018

In‐memory integration of existing software components for parallel adaptive unstructured mesh workflows

Summary Reliable mesh‐based simulations are needed to solve complex engineering problems. Mesh adaptivity can increase reliability by reducing discretization errors but requires multiple software components to exchange information. Often, components exchange information by reading and writing a common file format. This file‐based approach becomes a problem on massively parallel computers where filesystem bandwidth is a critical performance bottleneck. Our approach using data streams and component interfaces avoids the filesystem bottleneck. In this paper, we present these techniques and their use for coupling mesh adaptivity to the PHASTA computational fluid dynamics solver, the Albany multi‐physics framework, and the Omega3P linear accelerator frequency analysis applications. Performance results are reported on up to 16,384 cores of an Intel Knights Landing‐based system.

Smith, Cameron W.↗
DOE OSTI2021

A moving morphable component‐based topology optimization approach considering transient structural dynamic responses

Abstract A novel approach is developed under the moving morphable component (MMC) framework to solve topology optimization problems considering transient structural dynamic responses, where the structure for optimization is constructed with a collection of structural components, and the structural layout can be described explicitly via only a small number of design variables characterizing the positions and geometries of the components. The transient dynamic response of the structure and corresponding sensitivities are computed via step‐by‐step numerical integration and mode reduction technique is also employed to enhance the computational efficiency. Compared with existing works, the numerical instabilities induced by localized vibration modes can be substantially alleviated and the computational effort for direct sensitivity analysis is significantly reduced in the proposed approach. Two and three‐dimensional numerical examples considering the cases of nonproportional damping mechanism and time‐varying mass are also provided to manifest the validity of the proposed approach.

Li, Jialin↗
DOE OSTI2023

Coconut oil derived five‐component synthetic oviposition deterrent for oriental fruit fly, Bactrocera dorsalis

Abstract BACKGROUND Bactrocera dorsalis, oriental fruit fly (OFF), is one of the most destructive agricultural pests. Although bait sprays can effectively control OFF,resistance development has been a concern. We evaluated the oviposition deterrent activity of coconut free fatty acids (CFFA), a mixture of eight coconut oil‐derived fatty acids known to repel hematophagous insects and deter their feeding and oviposition, against OFF females. RESULTS In laboratory 72‐h two‐choice assays using guava‐juice infused‐agar as an oviposition substrate, CFFA deterred OFF oviposition in a dose‐dependent manner with the greatest reduction of 87% at 20 mg dose compared to the control. When the eight CFFA components were tested individually, four compounds (caprylic, capric, oleic, and linoleic acids) significantly reduced OFF oviposition (‘negative‐compounds’), two (lauric and myristic acids) had no effect (‘neutral‐compounds’), and two (palmitic and stearic acids) stimulated OFF oviposition (‘positive‐compounds’). In two‐choice tests, the ‘negative‐compounds’ blend failed to elicit the same level of oviposition reduction as CFFA at equivalent concentrations found in CFFA. Adding the two ‘neutral‐compounds’ recovered the oviposition deterrence similar to CFFA. Subsequent subtraction tests showed that four ‘negative‐compounds’ plus lauric acid was as effective as CFFA in reducing OFF oviposition in guava‐juice agar. This five‐component key‐deterrent blend also reduced OFF oviposition by 95 and 72% on papaya and tomato fruit, respectively. CONCLUSION CFFA acts as an oviposition deterrent for OFF. Given that CFFA compounds are generally regarded as safe for humans and the environment, CFFA and its bioactive components have potential use in behavioral control strategies against OFF. © 2023 Society of Chemical Industry. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Agriculture↗
DOE OSTI2020

Nonlinear Logistic Regression Mixture Experiment Modeling for Binary Data Using Dimensionally Reduced Components

This article presents and illustrates an approach using nonlinear logistic regression for modeling binary response data from a mixture experiment when the components can be partitioned into groups used to form dimensionally-reduced pseudocomponents (DRPs). A DRP is a linear combination of the components in a group, where the linear combinations over all groups are normalized so that the DRP proportions sum to unity. Nonlinear logistic regression is required because, after normalization of the linear combinations, the model expressed in terms of the DRPs is nonlinear in the parameters that specify the linear combinations. A method for obtaining nonparametric tolerance limits on the probability of a “success” for the binary response variable using a bootstrap approach is also presented. Having three DRPs enables viewing data and modeling results on a ternary plot even though there may be many more than three mixture components. A real database, involving whether or not nepheline crystals form in simulated nuclear waste glass after cooling, is used to illustrate the nonlinear logistic regression modeling and nonparametric tolerance limit approaches when there are three DRPs.

Waste glass, Nepheline, Mixture experiment, Logist↗
DOE OSTI2019

Build optimization of fiber-reinforced additively manufactured components

Additive manufacturing (AM) is enabling the fabrication of artifacts with unprecedented geometric and material complexity. The focus of this paper is on the build the optimization of short fiber reinforced polymers (SFRP) AM components. Specifically, we consider optimization of the build direction, topology, and fiber orientation of SFRP components. All three factors have a significant impact on the functional performance of the printed part. While significant progress has been made on optimizing these independently, the objective of this paper is to consider all three factors simultaneously and explore their interdependency, within the context of thermal applications. Towards this end, the underlying design parameters are identified, appropriate sensitivity equations are derived, and a formal optimization problem is posed as an extension to the popular Solid Isotropic Material with Penalization (SIMP). Results from several numerical experiments are presented, highlighting the impact of build direction, topology, and fiber orientation on the performance of SFRP components.

42 ENGINEERING↗
DOE OSTI2018

Engineering Analysis of Thermal-Load Components in the Process of Heating of Pet Preforms

The influence of thermal-load components (convection, collimated and uncollimated components of infrared radiation) in the process of production of PET packaging on the heating of PET preforms has been assessed. It has been established that the collimated component of infrared radiation ensures most (up to 70%) of the thermal energy in the process of heating of a PET preform.

42 ENGINEERING↗
DOE OSTI2025

Multiscale Characterization of Additive Manufacturing Components with Computed Tomography, 3D X-ray Microscopy, and Deep Learning

Additive manufacturing (AM) facilitates the creation of complex-geometry parts, driving advancements in lightweight aerospace components, high-efficiency engine cooling channels, and customized medical implants. However, ensuring the quality and reliability of AM parts remains challenging due to internal defects, surface irregularities, porosity, and residual trapped powder, which are often inaccessible to traditional inspection methods. Recent developments in X-ray computed tomography (XCT) and 3D X-ray microscopy (XRM), particularly systems equipped with resolution-at-a-distance (RaaD™) capabilities, enable high-resolution, non-destructive evaluation of AM components across multiple scales, from sub-micrometer to macroscopic levels. This paper explores modern XCT and XRM techniques for multiscale characterization of AM parts, focusing on their ability to detect and analyze defects such as porosity, cracks, inclusions, and surface roughness, while offering insights into defect formation mechanisms, material properties, and process-induced variations. The integration of deep learning (DL) frameworks, including Simurgh, DeepRecon, and DeepScout, enhances XCT/XRM workflows by reducing scan times, improving resolution recovery, and enabling accurate defect detection even with limited projection data. These DL-based methods overcome limitations of traditional reconstruction techniques, enabling faster, more reliable characterization of dense materials like Inconel 718 and novel alloys such as AlCe. Applications include process parameter optimization, high-throughput quality control, and multistage AM process evaluation, with DL-enhanced workflows accelerating analysis times from weeks to days. Correlative imaging approaches further validate XCT and XRM data against scanning electron microscopy (SEM) images of physically sectioned samples, confirming the accuracy of DL-based reconstructions and enabling comprehensive defect analysis. While challenges remain in generalizing DL models to diverse materials and imaging conditions, improvements in resolution, noise reduction, and defect detection highlight the transformative potential of these methods. This multiscale and correlative approach enables precise identification and correlation of microstructural features with the overall performance of AM components. By integrating advanced XCT, XRM, and DL techniques, this paper demonstrates a significant leap forward in AM characterization, offering valuable insights into the relationships between processing parameters, microstructure, and part performance, and driving innovations that enhance the quality and reliability of AM products for demanding industrial applications.

Additive manufacturing↗
DOE OSTI2018

Near Critical Preferential Attachment Networks have Small Giant Components

Preferential attachment networks with power law exponent τ>3 are known to exhibit a phase transition. There is a value ρ{sub c}>0 such that, for small edge densities ρ≤ρ{sub c} every component of the graph comprises an asymptotically vanishing proportion of vertices, while for large edge densities ρ>ρ{sub c} there is a unique giant component comprising an asymptotically positive proportion of vertices. In this paper we study the decay in the size of the giant component as the critical edge density is approached from above. We show that the size decays very rapidly, like exp (−c/√(ρ−ρ{sub c})) for an explicit constant c>0 depending on the model implementation. This result is in contrast to the behaviour of the class of rank-one models of scale-free networks, including the configuration model, where the decay is polynomial. Our proofs rely on the local neighbourhood approximations of Dereich and Mörters (Ann Probab 41(1):329–384, 2013) and recent progress in the theory of branching random walks (Gantert et al. in Ann Inst Henri Poincaré Probab Stat 47(1):111–129, 2011).

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Investigation of the Window Variance Noise Component of Multicomponent Signals

Signal partitioning or signal segmentation allows to perform data classification, prediction of signals’ behavior, and profound interpretation of obtained data. In accordance with the signal model some distribution characteristics of the window variance noise component are investigated. It was shown that when all true underlying signal components remain unchanged, the window variance noise component is gamma distributed. Applying window variance to multicomponent signal (i.e., pharmacokinetic curve), it was shown that the window variance allows to split visually the signal record into phases due to prevalent processes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Interaction between the emerging components of online shopping and in-person activities: insights from a behavioral survey

The rise of technological advancements has led to the commonplace practice of online shopping for retail, grocery, and food. However, little research has been conducted on the interplay of these components in burdened communities (BCs) that face issues of marginalization and limited access to digital resources. Here, this study aims to provide a comprehensive understanding of travel behavior changes by analyzing the interconnectedness of the emerging components of online shopping (retail, grocery, and food) and in-person activities in both BCs and non-BCs. A unique household-level database is created by linking the 2021 Puget Sound Household Travel Survey and the US Department of Transportation’s burdened community databases, and a conditional mixed process model is estimated to account for unobserved endogeneity. The findings suggest households living in BCs are less likely to order online retail goods and groceries compared to non-BC households. Additionally, the probability of making more restaurant trips decreases for households living in BCs. The study highlights the digital divide that exists in BCs and the differences in online and in-person shopping activities across socioeconomic levels. Policymakers may address these disparities to promote better access to goods and services for all. Besides, planners may need to improve the travel demand models by accounting for the emerging components of online shopping and the trip frequencies by purpose in BCs.

Digital Divide↗
DOE OSTI2019

A Reaction Between High Mn–High Al Steel and CaO-SiO{sub 2}-Type Molten Mold Flux: Reduction of Additive Oxide Components in Mold Flux by Al in Steel

The interfacial reaction between high-Mn–high-Al steel and CaO-SiO{sub 2}-type mold flux was investigated, with particular emphasis on the reduction of additive oxide components by Al. Typical additive oxide components (B{sub 2}O{sub 3}, Na{sub 2}O) could be reduced by Al, thereby losing their initial role in the flux. It is proposed to consider the chemical stability of the additive components in the flux in addition to the physico-chemical properties of the flux when developing the mold flux chemistry.

36 MATERIALS SCIENCE↗
DOE OSTI2019

A New Efficient Quantitative Multi-component Phase Field: Lattice Boltzmann Model for Simulating Ti6Al4V Solidified Dendrite Under Forced Flow

Ti6Al4V is a widely used, multi-component alloy in additive manufacturing, during which the fluid flow in the molten pool significantly affects the solidified dendrites. To predict and further control the microstructure, modeling and simulating the microstructure evolution play a critical role. In this study, a newly developed, efficient, quantitative multi-component phase-field (PF) model is coupled with a lattice Boltzmann (LB) model to simulate Ti6Al4V solidified dendrite evolution under fluid flow. The accuracy and convergence behavior of the model is validated by the Gibbs–Thomson relation at the dendrite tip. Single and multiple two-dimensional (2D) equiaxed dendrite evolution cases under forced flow were simulated. Results show that the dendrite pattern is influenced remarkably by the fluid flow. Underlying mechanisms of the asymmetrical evolution are revealed by discussing the interaction among the flow, composition distribution and dendrite morphology, quantitatively. The dendrite kinetics are also derived, which ascertains the relationship between tip velocity and undercooling and inlet velocity and is the foundation for larger-scale simulation. We believe that the coupled quantitative multi-component PF–LB framework employed in this study helps in investigating the solidified dendrite morphology evolution in a deep and quantitate manner.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Multimodal Defect Imaging of Pure Tungsten Components Fabricated via Electron Beam Powder Bed Fusion

The utilization of additive manufacturing (AM) techniques for refractory materials in high-temperature environments has significantly expanded because of the ability to fabricate geometrically complex components. Electron beam powder bed fusion (EB-PBF), which provides lower residual stress, a cleaner vacuum environment, and better efficiency for high melting point, is one of the best-suited AM methods to produce advanced refractory components. However, the property variation attributed to the heterogeneous microstructure and process-induced defects has hindered the widespread adoption of EB-PBF-produced material like tungsten. While numerous in-situ monitoring and defect detection methods have been demonstrated for EB-PBF, a workflow that compares and evaluates process-induced abnormalities from different imaging perspectives is still limited. This study examines a feature-embedded tungsten component manufactured via the EB-PBF process to demonstrate the defect detection capabilities of a multimodal defect imaging workflow. The predefined and process-induced defects are evaluated by harnessing various imaging techniques, including in-situ electron imaging, layerwise near-infrared (NIR) imaging, post-build high-energy x-ray computed tomography (CT), and conventional destructive metallography. The results highlight the strengths and limitations of distinctive defect imaging techniques concerning specific defect types, sizes, and conditions. It was found that electron imaging can provide more abnormal detection capabilities while maintaining a higher measuring accuracy, against the conventional metallography in this case study, compared with NIR and CT imaging techniques.

36 MATERIALS SCIENCE↗
DOE OSTI2018

An Improved Sub-component Fatigue Testing Method for Material Characterization

In this paper an improved sub-component fatigue testing method is proposed, in which structural optimization is used to obtain specimens in which fatigue failure is precipitated in the designated area away from the boundaries, i.e., load application and fixture points. This is achieved by optimizing the nonlinear beam taper and the dynamic excitation. The major requirement for accurate material characterization through sub-component tests concerns unbiased stress states in the gauge section. However, empiricism shows that many sub-component high-cycle fatigue testing methods suffer from failure in the boundaries rather than the gauge section, which causes bias. The common practice for reinforcing those regions only shifts the issue into new areas of local discontinuities where failure is still caused remotely from the gauge section. An experimental proof of concept demonstrates that optimization of the beam taper can be used to obtain unbiased fatigue test data.

42 ENGINEERING↗
DOE OSTI2018

Molecular identification of BrHAB2a, one of the two AtHAB2-like proteins in Brassica rapa, is an important component of ABA signaling

Highlights: • Fourteen putative PP2C-A-like phosphatases were identified in Brassica rapa. • BrHAB2a was verified as a putative negative regulator of ABA signaling. • The inhibition of BrHAB2a by ABA and its receptors was successfully recapitulated. Abscisic acid (ABA) signaling is a vital physiological step that is used by many land plants to fight against environmental stress. As components of the linear ABA signaling pathway, clade A protein phosphatases type 2C (PP2C-As) are mainly inhibited by PYRABACTIN RESISTANCE1/PYR1-LIKE/REGULATORY COMPONENTS OF ABA RECEPTORS (PYLs)-type receptors upon their binding to ABA. Here, we show that the genome of Brassica rapa encodes 14 putative clade A PP2C-like proteins (BrPP2C-As). Two of these BrPP2C-As, Bra025964 and Bra016595, show high similarity to the HAB2 (Homology to ABI2) protein in Arabidopsis. RNAseq data reveal that nearly all BrPP2C-As, like BrHAB2a (Bra025964) and BrHAB2b (Bra016595), were highly expressed in at least one tissue. Overexpression of BrHAB2a conferred ABA insensitivity to Arabidopsis thaliana seedlings. Furthermore, the phosphatase activity of BrHAB2a could be inhibited by AtPYL1 or BrPYL1 in the presence of ABA. Overall, these results suggest that BrHAB2a is a functional PP2C-A like protein phosphotase and a key component of ABA signaling in Brassica rapa.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Measurement and prediction of through-section residual stresses in the manufacturing sequence of bearing components

Through-section residual stresses in an asymmetric, thin-walled tapered bearing component were non-destructively studied via neutron diffraction after soft machining, heat treatment and hard turning. Two-dimensional area maps revealed non-uniform distributions of stress fields in both the thinnest and thickest sections of the component, indicating that the entire processing sequence plays a vital role — not only in distortion, but in the final stress state as well. Here, a computational model was developed to further understand the extent of the distortion induced in bearing components as a result of process variables in the manufacturing sequence.

36 MATERIALS SCIENCE↗
DOE OSTI2018

A new chemical kinetic method of determining RON and MON values for single component and multicomponent mixtures of engine fuels

A new method of using chemical kinetic reaction modeling to predict the Research Octane Number (RON) and Motor Octane Number (MON) of single component fuels and fuel mixtures is described and illustrated via comparisons between computed and experimental values obtained using the well-established ASTM test procedures in a Cooperative Fuels Research (CFR) engine. Comparisons include predictions of RON and MON for a large variety of neat fuels, studies determining the RON and MON of mixtures of primary reference fuels (PRF) and toluene, and studies of RON and MON for mixtures of single-component and multiple-component gasoline surrogate mixtures with ethanol. Advantages in costs, time, and experimental complexity of the kinetic modeling approach compared to the existing engine test procedures are discussed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗