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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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Technique is used for evaluation and characterization of materials, fluids, and biological tissue. Method eliminates problem of electrical drive pulse shape by slaving tracking generator to local oscillator of spectrum analyzer. Logic/timing generator is used to control pulse transmission and receiving sequence, pulse width, and pulse repetition rate.
Physical properties of fiber reinforced composites; structural concepts and analysis; manufacturing; reliability; and life prediction are subjects of research conducted to determine the long term integrity of composite aircraft structures under conditions pertinent to service use. Progress is reported in (1) characterizing homogeneity in composite materials; (2) developing methods for analyzing composite materials; (3) studying fatigue in composite materials; (4) determining the temperature and moisture effects on the mechanical properties of laminates; (5) numerically analyzing moisture effects; (6) numerically analyzing the micromechanics of composite fracture; (7) constructing the 727 elevator attachment rib; (8) developing the L-1011 engine drag strut (CAPCOMP 2 program); (9) analyzing mechanical joints in composites; (10) developing computer software; and (11) processing science and technology, with emphasis on the sailplane project.
A method for characterizing an anomaly in a material comprises (a) extracting contrast data; (b) measuring a contrast evolution; (c) filtering the contrast evolution; (d) measuring a peak amplitude of the contrast evolution; (d) determining a diameter and a depth of the anomaly, and (e) repeating the step of determining the diameter and the depth of the anomaly until a change in the estimate of the depth is less than a set value. The step of determining the diameter and the depth of the anomaly comprises estimating the depth using a diameter constant C.sub.D equal to one for the first iteration of determining the diameter and the depth; estimating the diameter; and comparing the estimate of the depth of the anomaly after each iteration of estimating to the prior estimate of the depth to calculate the change in the estimate of the depth of the anomaly.
Principal Component Analysis (PCA) has been shown effective for reducing thermographic NDE data. While a reliable technique for enhancing the visibility of defects in thermal data, PCA can be computationally intense and time consuming when applied to the large data sets typical in thermography. Additionally, PCA can experience problems when very large defects are present (defects that dominate the field-of-view), since the calculation of the eigenvectors is now governed by the presence of the defect, not the "good" material. To increase the processing speed and to minimize the negative effects of large defects, an alternative method of PCA is being pursued where a fixed set of eigenvectors, generated from an analytic model of the thermal response of the material under examination, is used to process the thermal data from composite materials. This method has been applied for characterization of flaws.
Electroactive polymers (EAP) are emerging as a new class of actuation materials being considered in a wide range of applications.
The recent introduction of deep learning methods for image processing has greatly advanced the characterization of materials using three-dimensional (3D) X-ray imaging techniques. However, deep learning models often have difficulty performing consistently across images owing to unavoidable variations in imaging conditions, which create inconsistencies even for the same material. As a result, networks must frequently be retrained for new datasets, limiting their applicability and generalization. Thus, it is critical to reduce the variations between images to enable a single model to process multiple datasets. Herein, we introduce P3T-Net, a pseudo-3D domain transfer network that transfers diverse 3D images into a uniform domain before processing using deep learning models. Remarkably, P3T-Net enables the reuse of previously trained networks for processing new images and considerably reduces the computational cost of transferring 3D images across domains. These unique capabilities were demonstrated in the following scenarios: (i) image enhancement of fast scans for geological rock and hydrogen fuel cells, (ii) enhancement of images to match the quality of multi-source imaging for lithium-ion batteries, (iii) accurate segmentation of images captured under different conditions, and (iv) tera-scale 3D transfer (10 11 voxels) on a single GPU. Overall, the proposed approach addresses cross-domain inconsistencies across various materials and conditions, thereby enabling more robust and generalizable deep learning solutions for a wide range of material imaging tasks.
The purpose of this “Low Density Steels for Transportation Applications” project was to develop an alloy composition and processing parameters that would result in a material suitable for use in automotive structural components at a reduced density over the current advanced high strength steel (AHSS) materials used. The project work successfully developed a robust alloy capable of exceeding project mechanical property targets at each stage of development, with an 8% density reduction over benchmark AHSS materials (7.8 g/cm3). The developed alloy has the potential to offer significant vehicle lightweighting and improved fuel economy, without sacrificing the increased passenger safety of more traditional AHSS. Through the three tasks of the project, (1) Alloy design and small-scale laboratory evaluation, (2) Laboratory development of hot rolled material and (3) Laboratory development of a cold rolled material, the laboratory work utilized advanced characterization and analytical methods on novel alloy compositions subjected to both conventional and non-conventional processing operations.
When characterizing materials using transmission electron microscopy (TEM) images, detecting and quantifying small features in microstructures, such as cavities, pose significant challenges. Off-the-shelf object detection models, including YOLOv8, show considerable performance degradation, particularly when images vary in resolution and the objects of interest possess a low percentage of the total image region of interest. In this study, we introduce a novel detection pipeline that incorporates an implicit neural representation (INR)-based detection method, INR-TEM, and two-modality imaging (e.g., under-focused and over-focused images typically acquired during materials characterization) to improve object detection performance. The INR-TEM method incorporates a pixel-wise prediction principle inspired by pixel-wise centerness weighting. INR-TEM demonstrates superior robustness to resolution variability, maintaining high detection accuracy even at low image resolutions compared to YOLOv8. To leverage INR-TEM effectively in real-world two-modality characterization applications, we further integrate a two-stage motion correction pipeline designed explicitly for aligning multifocus TEM images. The alignment process, comprising keypoint (based on scale-invariant feature transform, SIFT) and intensity matching, significantly mitigates the adverse effects of perceived motion-induced image degradation during through-focal TEM imaging, directly enhancing INR-TEM’s detection capability over conventional single-focus images. Our integrated INR-TEM cavity detection framework notably improves performance across various cavity sizes, outperforming off-the-shelf YOLOv8 detections that rely on a single image modality.
Fiber-optic sensing has shown promising development for use in detecting magnetic fields for downhole and biomedical applications. Coupling existing fiber-based strain sensors with highly magnetostrictive materials allows for a new method of magnetic characterization capable of distributed and high-sensitivity field measurements. This study investigates the strain response of the highly magnetostrictive alloys Metglas® 2605SC and Vitrovac® 7600 T70 using Fiber Bragg Grating (FBG) acoustic sensors and an applied AC magnetic field. Sentek Instrument’s picoDAS interrogated the distributed FBG sensors set atop a ribbon of magnetostrictive material, and the corresponding strain response transferred to the fiber was analyzed. Using the Vitrovac® ribbon, a minimal detectable field amplitude of 60 nT was achieved. Using Metglas®, an even better sensitivity was demonstrated, where detected field amplitudes as low as 3 nT were measured via the strain response imparted to the FBG sensor. Distributed FBG sensors are readily available commercially, easily integrated into existing interrogation systems, and require no bonding to the magnetostrictive material for field detection. The simple sensor configuration with nanotesla-level sensitivity lends itself as a promising means of magnetic characterization and demonstrates the potential of fiber-optic acoustic sensors for distributed measurements.
This final report summarizes the results of the Laboratory Direct Research and Development (LDRD) Project Number 229740. Wide band gap semiconductors such as gallium nitride (GaN) have features highly desirable for multiple mission electronic applications. Realization of their potential requires atomic-scale understanding of electronic behavior. The principal experimental tools for electronically probing defects in GaN are chemically undifferentiating and lack a practical theoretical counterpart needed to identify and characterize specific defects. This project investigated whether a simple idea for modeling defect excited states and their associated photoluminescence (PL) energies is viable, as a path to accelerate the understanding of defect behavior and gain valuable insights into engineering new electronic materials and devices. The research implemented a non-self-consistent total-energy evaluation of a Koopmans-type estimation of an excited electronic state energy in density functional theory (DFT) calculations, and proceeded to design, implement, and assess a self-consistent method for computing excited states based upon an OCcupation-Constrained-DFT (occ-DFT). The occ-DFT was verified in test calculations of defect excited states and validated against well-characterized PL data for 3d transition metal defects in GaN. The method proved stable and robust in computing excited states and gave accurate predictions compared to experimental PL data. The combined ground state/excited-state capability proved capable of chemically differentiating defect species in GaN. In application to 3d dopants in GaN, we reinterpreted extensive experimental literature, proposed new defects as prospective candidates for use in quantum information applications, and outlined design strategies to create and exploit these potentially useful functional defects in GaN.
Savannah River National Laboratory (SRNL) has developed expertise in producing homogeneous, ca. 1 m-diameter spherical particles of mixed-element components, wherein dopants can be varied from a trace constituent (ppm) to wt.% concentrations. The samples used for this work are nickel-doped cerium oxide microspheres produced by SRNL. They were initially selected as analogs for plutonium-doped uranium oxide particles and analyzed as part of a larger study to evaluate whether electron probe microanalyzers (EPMA) can be used to characterize nuclear materials as an alternative or complementary method to mass spectrometers. The five samples used in this study contained nominal compositions of 0, 0.004, 0.04, 0.4 and 4 wt.% Ni. They were analyzed by both an Agilent 7900 Q-ICP-MS at SRNL and the JEOL JXA8530F Plus EPMA at the University of Minnesota. In addition to EPMA results (calibrated with high-precision Q-ICP-MS analyses) suggesting that the EPMA could address outstanding nuclear material characterization needs, these samples 1) showcase the ability of the EPMA to quantify not just trace concentrations, but trace concentrations in microparticles (1 m diameter, Fig. 1), and 2) offer a unique opportunity to evaluate the methodology for assessing the minimum detection limits of EPMA analyses.
Uranium nitride and carbide-based fuels are proposed for use in advanced LEU fuel systems. This report details recent development work to study synthesis, sintering and fabrication methods for single phase UN in monolithic geometries. These samples were intended to support a variety of irradiation testing conditions by tailoring enrichment, geometry, and compositional requirements. Two methods were under evaluation for synthesizing uranium nitride powder – carbothermic reduction-nitridation (CTR-N) of UO 2 powder feedstock and hydride-dehydride/nitridation (HDN) of uranium metal feedstock. Pellets were fabricated from powder feedstocks via conventional pressing and sintering methods. As-fabricated feedstocks and pellets were evaluated via X-ray diffraction (XRD) for phase purity and sintered materials were characterized for density. Both the CTR N and the HDN methods were successfully demonstrated as an effective process for fabricating phase pure UN. Additionally, results from machining efforts for UN are detailed. This report also contains information on a study trying to elucidate the oxidation mechanisms of sintered uranium carbide samples fabricated from feedstock synthesized via an arc-melting method.
Machine learning methods are emerging as a universal paradigm for constructing correlative structure–property relationships in materials science based on multimodal characterization. However, this necessitates the development of methods for the physical interpretability of the resulting correlative models. Here, we demonstrate the potential of attention-based neural networks for revealing structure–property relationships and the underlying physical mechanisms, using the ferroelectric properties of PbTiO3 thin films as a case study. Through the analysis of attention scores, we disentangle the influence of distinct domain patterns on the polarization switching process. The attention-based Transformer model is explored both as a direct interpretability tool and as a surrogate for explaining representations learned via unsupervised machine learning, enabling the identification of physically grounded correlations. We compare attention-derived interpretability scores with classical SHapley Additive exPlanations analysis and show that, in contrast to applications in natural language processing, attention mechanisms in materials science exhibit high efficiency in highlighting meaningful structural features.
Ultrasonic damage detection and characterization is commonly used in nondestructive evaluation (NDE) of aerospace composite components. In recent years there has been an increased development of guided wave based methods. In real materials and structures, these dispersive waves result in complicated behavior in the presence of complex damage scenarios. Model-based characterization methods utilize accurate three dimensional finite element models (FEMs) of guided wave interaction with realistic damage scenarios to aid in defect identification and classification. This work describes an inverse solution for realistic composite damage characterization by comparing the wavenumber-frequency spectra of experimental and simulated ultrasonic inspections. The composite laminate material properties are first verified through a Bayesian solution (Markov chain Monte Carlo), enabling uncertainty quantification surrounding the characterization. A study is undertaken to assess the efficacy of the proposed damage model and comparative metrics between the experimental and simulated output. The FEM is then parameterized with a damage model capable of describing the typical complex damage created by impact events in composites. The damage is characterized through a transdimensional Markov chain Monte Carlo solution, enabling a flexible damage model capable of adapting to the complex damage geometry investigated here. The posterior probability distributions of the individual delamination petals as well as the overall envelope of the damage site are determined.
A general method for characterizing structual adhesives in the bonded lap shear mode is proposed. Two approaches in the form of semiempirical and theoretical approaches are used. The semiempirical approach includes Ludwik's and Zhurkov's equations to describe respectively, the failure stresses in the constant strain rate and constant stress loading modes with the inclusion of the temperature effects. The theoretical approach is used to describe adhesive shear stress-strain behavior with the use of viscoelastic or nonlinear elastic constitutive equations. Two different model adhesives are used in the single lap shear mode with titanium adherends. These adhesives (one of which was developed at NASA Langley Research Center) are currently considered by NASA for possible aerospace applications. Use of different model adhesives helps in assessment of the generality of the method.
A general method for characterizing structural adhesives in the bonded lap shear mode is proposed. Two approaches in the form of semi-empirical and theoretical approaches are used. The semi-empirical approach includes Ludwik's and Zhurkov's equations to describe respectively, the failure stresses in the constant strain rate and constant stress loading modes with the inclusion of the temperature effects. The theoretical approach is used to describe adhesive shear stress-strain behavior with the use of viscoelastic or nonlinear elastic constitutive equations. Three different model adhesives are used in the simple lap shear mode with titanium adherends. These adhesives (one of which was developed at NASA Langley Research Center) are currently considered by NASA for possible aerospace applications. Use of different model adhesives helps in assessment of the generality of the method.
One of the performance goals for NASA's enterprise of Human Exploration and Development of Space (HEDS) is to develop methods, data bases, and validating tests for material flammability characterization, hazard reduction, and fire detection/suppression strategies for spacecraft and extraterrestrial habitats. This work addresses these needs by applying the fundamental knowledge gained from low stretch experiments to the development of a normal gravity low stretch material flammability test method. The concept of the apparatus being developed uses the low stretch geometry to simulate the conditions of the extraterrestrial environment through proper scaling of the sample dimensions to reduce the buoyant stretch in normal gravity. The apparatus uses controlled forced-air flow to augment the low stretch to levels which simulate Lunar or Martian gravity levels. In addition, the effect of imposed radiant heat flux on material flammability can be studied with the cone heater. After breadboard testing, the apparatus will be integrated into NASA's White Sands Test Facility's Atmosphere-Controlled Cone Calorimeter for evaluation as a new materials screening test method.
Two-dimensional (2D) van der Waals materials are shaping the landscape of next-generation devices, offering significant technological value thanks to their unique, tunable, and layer-dependent electronic and optoelectronic properties. Time-domain spectroscopic techniques at terahertz (THz) frequencies offer noninvasive, contact-free methods for characterizing the dynamics of carriers in 2D materials. They also pave the path toward the applications of 2D materials in detection, imaging, manufacturing, and communication within the increasingly important THz frequency range. In this paper, we overview the synthesis of 2D materials and the prominent THz spectroscopy techniques: THz time-domain spectroscopy, optical-pump THz-probe technique, and optical pump–probe THz spectroscopy. Through a confluence of experimental findings, numerical simulation, and theoretical analysis, we present the current understanding of the rich ultrafast physics of technologically significant 2D materials: graphene, transition metal dichalcogenides, MXenes, perovskites, topological 2D materials, and 2D heterostructures. Finally, we offer a perspective on the role of THz characterization in guiding future research and in the quest for ideal 2D materials for new applications.