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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
NASA NTRS1992

Piezoelectric Measurement Of Bulk Modulus

In method of measuring bulk modulus of elasticity of elastomeric material, piezoelectric crystals of various sizes and energized by alternating voltage embedded in material. Concept demonstrated in test cell in which piezoelectric crystal mounted either unconstrained or between two rubber pads and connected as actuator in loud-speaker. The 1-in. diameter crystal excited with 24 Vac at 60 Hz. When crystal was unconstrained, it drew current of 0.8 mA. When crystal was constrained between rubber pads, current fell to 0.65 mA. Low current, minimal heating, and absence of arcing makes technique suitable for measurement of bulk moduli of elasticity of flammable or explosive rubbery materials.

Butler, Barry L.↗
NASA NTRS1992

Crack-mouth displacements for semielliptical surface cracks subjected to remote tension and bending loads

The exact analytical solution for an embedded elliptical crack in an infinite body subjected to arbitrary loading was used in conjunction with the finite element alternating method to obtain crack-mouth-opening displacements (CMOD) for surface cracks in finite plates subjected to remote tension. Identical surface-crack configurations were also analyzed with the finite element method using 20-noded element for plates subjected to both remote tension and bending. The CMODs from these two methods generally agreed within a few percent of each other. Comparisons made with experimental results obtained from surface cracks in welded aluminum alloy specimens subjected to tension also showed good agreement. Empirical equations were developed for CMOD for a wide range of surface-crack shapes and sizes subjected to tension and bending loads. These equations were obtained by modifying the Green-Sneddon exact solution for an elliptical crack in an infinite body to account for finite boundary effects. These equations should be useful in monitoring surface-crack growth in tests and in developing complete crack-face-displacement equations for use in three-dimensional weight-function methods.

Raju, Ivatury S.↗
NASA NTRS1991

A flow-control mechanism for distributed systems

A new approach to the rate-based flow control in store-and-forward networks is evaluated. Existing methods display oscillations in the presence of transport delays. The proposed scheme is based on the explicit use of an embedded dynamic model of a store-and-forward buffer in a controller's feedback loop. It is shown that the use of the model eliminates the oscillations caused by the transport delays. The paper presents simulation examples and assesses the applicability of the scheme in the new generation of high-speed photonic networks where transport delays must be considered.

Maitan, J.↗
NASA NTRSDate not supplied

Initial analysis of “stone” size Ryugu samples: current status

As a part of the initial analysis of the Ryugu samples, we perform a variety of analyses of millimeter-sized "stones". Our goals are to elucidate the entire formation process of C-type asteroid Ryugu from the viewpoint of petrology and mineralogy ande obtain necessary information by sample analysis, and then simulate the formation of Ryugu based on the evidencef obtained rom sample analysis. Eighteen stones (8 from Room A and 10 from Room C) were received from the ISAS curation facility on June 1, 2021, and brought into a fully nitrogen-displaced glove box at Tohoku University. At the same time, we also received the powder samples from Room A and Room C. To completely block the atmosphere from leaking into the container, all samples were put in the sample transport containers prepared by ISAS, transferred from the main chamber to the glovebox at ISAS, and then all containers were completely sealed in plastic bags with moisture and oxygen absorbers. No moisture or oxygen was detected when the bags were opened in the glove box at Tohoku University, so it was confirmed that there was no exposure to the atmosphere during transport. To date, a number of analyses have been carried out successfully and almost on schedule. The analysis started with the measurement of reflectance spectra, which are sensitive to atmospheric oxidation, hydroxylation, and adsorbed water. The visible, near-infrared, and mid-infrared reflectance spectra were measured while the samples were kept airtight. The spectra of powder samples and stone samples (as aggregates and as single stone) were successfully obtained. A major feature of the stone team's analysis is the use of synchrotron radiation facilities around the world. Since this analysis is non-destructive, stone samples whose reflectance spectra were measured were sent to KEK, SPring-8, ESRF (France), SOLEIL (France), DESY (Germany), and APS (USA). Using these synchrotron radiation facilities, high spatial resolution and sensitivity XRD, STXM, XANES, CT [1], IR-CT, FT-IR [2, 3], XRF, and Mössbauer analyses were performed. Most of the analyses were carried out under air-tight conditions on the stone samples and the particulates separated from the stone samples. These analyses allowed us to determine the three-dimensional distribution of minerals and elements, redox state, density and porosity of the stone samples. Furthermore, as a characteristic analysis of the stone team, light elemental analysis using negative Muon was performed at the MLF facility of J-PARC with an exceptionally long allocation of machine time. This is the only non-destructive method to measure the concentration of light elements in the whole (not the surface) of stone samples. Because the characteristic X-rays produced by muon irradiation are much higher in energy than the fluorescent X-rays produced by X-ray irradiation, there is little effect of self-absorption by the sample, and therefore, the concentration of light elements such as carbon, oxygen, and Na in the entire "stone" sample can be determined. Some stone samples are currently being measured for heat and strength physical properties in order to understand the physical properties of asteroid Ryugu. The data obtained from these measurements are useful for interpreting the remote-sensing data data taken from the surface layer of the asteroid Ryugu [4-7]. It is also important for understanding the behavior of the Ryugu material during impact events. The surfaces of many stone samples were observed by electron microscopy and other techniques, especially on natural “flat” surfaces formed on 5 stomes. As a result, characteristric mineral aggregates formed by the reactions with water and characteiristic impact features were observed on some of the samples. Based on the observations of surfaces and the synchrotron measurements of the whole stones, important objects such as characteristic structures and specific crystal aggregates for understanding the formation history of the asteroid were identified, and these parts were separated from the stone samples using an Xe beam (pFIB) and analyzed by various methods including transmission electron microscopy and synchrotron radiation analysis. Many stone samples, from which important objects have been separated, are embedded in epoxy resin and cut to produce many polished sections. Electron microscopy and spectroscopic measurements of the polished surfaces are being carried out to reveal the detailed mineralogical properties and elemental distribution inside the stone samples. In the fall, machine time for synchrotron radiation will begin, and we plan to analyze single crystals and characteristic objects separated from the stone samples.

Tomoki Nakamura↗
DOE OSTI2024

Molecular Vision - Multimodal, multitask retrieval of molecular structure from measured signatures for reference-free compound identification

We are currently at risk of generating false conclusions based on limited methods to identify small molecules in biological systems and in chemical forensics. By definition, the chemical structures of novel small molecules have not been determined, let alone measured or synthesized. Currently, unambiguous structure determination of small molecules is constrained by the time and effort needed to isolate compounds and perform de novo structure elucidation using laboratory-based methods, significantly extending the time to inform mitigation strategies. To address this gap, we have developed a deep learning approach to directly map molecular structure to experimental signatures. We aim to unify measurement technologies employed in untargeted small molecule identification studies—such as infrared (IR) spectrometry, tandem mass spectrometry (MS/MS), ion mobility spectrometry-derived collision cross section (CCS)—through use of a multimodal, multitask deep learning architecture. Where existing methods require direct generation of information-rich spectra and/or properties, an inherently difficult task, we will simplify molecular signature-based identification by posing the problem as a recognition or retrieval task. The model is thus presented with relevant endpoints – structure and one or more molecular signatures – and need only determine whether they are semantically related. Thus, our approach offers the following advantages over existing techniques: (i) circumvents difficulties associated with direct generation of molecular signatures from structure and structure from signatures; (ii) incorporates multiple molecular signatures simultaneously, as available, to support identification; and (iii) enables rapid computation of structural embeddings toward broad coverage of known chemical space. Taken together, the approach removes the need to explicitly obtain or compute reference spectra, representing a powerful method for compound identification that requires only experimentally observed signatures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1976

Methods for testing high voltage connectors in vacuum, measurements of thermal stresses in encapsulated assemblies, and measurement of dielectric strength of electrodes in encapsulants versus radius of curvature

Internal embedment stress measurements were performed, using tiny ferrite core transformers, whose voltage output was calibrated versus pressure by the manufacturer. Comparative internal strain measurements were made by attaching conventional strain gages to the same type of resistors and encapsulating these in various potting compounds. Both types of determinations were carried out while temperature cycling from 77 C to -50 C.

Bever, R. S.↗
DOE OSTI2025

Leveraging Natural Language Processing and Generative Models in Molecular Chemistry: Property Prediction and Novel Compound Generation

The accurate prediction of molecular properties is important for the rational design and the advancement of green chemistry and sustainable materials research. However, the predictive power of traditional computational chemistry methods is limited due to computational restrictions. Here, in this study, we examine an alternative approach to the accurate prediction of properties of organic compounds: natural language processing (NLP)-based molecular embedding. Using viscosity, partition coefficient (log P), and enthalpy of vaporization as test properties through a survey of comprehensive datasets comprising 5695 data points for viscosity, 25 870 data points for log P, and 2296 data points for enthalpy of vaporization. These are important properties for the design of greener, safer, and sustainable chemical processes. Models were trained using NLP methods such as Mol2vec and fine-tuned ChemBERTa, and results were compared with traditional input featurization techniques such as Morgan fingerprints and quantum chemistry derived sigma profiles and DFT features. Among the various machine learning models, Mol2vec demonstrated superior predictive capabilities, achieving the highest correlation coefficient (R 2 = 0.945) and lowest RMSE (0.106 mPa s) for viscosity, as well as high accuracy for log P and enthalpy of vaporization predictions. These findings establish the Mol2vec featurization technique, graph-convolutional neural networks (GCNN), and fine-tuned ChemBERTa model as powerful tools for predictive modeling of organic compounds properties, offering a significant improvement over previously used featurization techniques and opening up strategies for very-high-throughput computational screening. Finally, we integrated ML models with hybrid language-model-based generative adversarial networks (LM-GAN) to generate novel molecular sequences with desirable properties for different research applications. The ability to computationally design solvents with lower viscosity, lower log P, and lower enthalpy of vaporization offers a data-driven route to accelerating the discovery of sustainable alternatives to traditionally toxic solvents.

ChemBERTa↗
NASA NTRS2004

Computer Tomography 3-D Imaging of the Metal Deformation Flow Path in Friction Stir Welding

In friction stir welding, a rotating threaded pin tool is inserted into a weld seam and literally stirs the edges of the seam together. This solid-state technique has been successfully used in the joining of materials that are difficult to fusion weld such as aluminum alloys. To determine optimal processing parameters for producing a defect free weld, a better understanding of the resulting metal deformation flow path is required. Marker studies are the principal method of studying the metal deformation flow path around the FSW pin tool. In our study, we have used computed tomography (CT) scans to reveal the flow pattern of a lead wire embedded in a FSW weld seam. At the welding temperature of aluminum, the lead becomes molten and thus tracks the aluminum deformation flow paths in a unique 3-dimensional manner. CT scanning is a convenient and comprehensive way of collecting and displaying tracer data. It marks an advance over previous more tedious and ambiguous radiographic/metallographic data collection methods.

Schneider, Judy↗
NASA NTRS2009

A Mode-Shape-Based Fault Detection Methodology for Cantilever Beams

An important goal of NASA's Internal Vehicle Health Management program (IVHM) is to develop and verify methods and technologies for fault detection in critical airframe structures. A particularly promising new technology under development at NASA Langley Research Center is distributed Bragg fiber optic strain sensors. These sensors can be embedded in, for instance, aircraft wings to continuously monitor surface strain during flight. Strain information can then be used in conjunction with well-known vibrational techniques to detect faults due to changes in the wing's physical parameters or to the presence of incipient cracks. To verify the benefits of this technology, the Formal Methods Group at NASA LaRC has proposed the use of formal verification tools such as PVS. The verification process, however, requires knowledge of the physics and mathematics of the vibrational techniques and a clear understanding of the particular fault detection methodology. This report presents a succinct review of the physical principles behind the modeling of vibrating structures such as cantilever beams (the natural model of a wing). It also reviews two different classes of fault detection techniques and proposes a particular detection method for cracks in wings, which is amenable to formal verification. A prototype implementation of these methods using Matlab scripts is also described and is related to the fundamental theoretical concepts.

Tejada, Arturo↗
NASA NTRSDate not supplied

Transfer function models for using empirical and physics-based simulation signal response data

In many situations, real or induced flaws such as tight cracks with known morphology cannot be manufactured in part geometry specimens or in real parts. Typically, surface fatigue cracks are manufactured in simple geometry specimens such as flat plates, dog-bone shaped flat or cylindrical specimens. If a nondestructive evaluation (NDE)technique is required to provide a reliably detectable flaw size, denoted asa90/95, for induced flaws in a part, then a direct method for qualifying the NDE procedure is to use appropriate induced flaw specimens and perform NDE procedure demonstration on the specimens. Probability of detection (POD)analysis of the empirical data may provide estimation of a90/95. This approach is described as direct POD demonstration testing, which may follow guidelines of MIL-HDBK-1823. This paper considers a case, where embedded tight cracklike induced flaws are to be detected reliably using a signal response based NDE procedure. Here, it is assumed that it is not practical to make surface or embedded induced flaw specimens in part geometry or configuration. Therefore, a direct POD demonstration testing cannot be undertaken. It is also assumed that simulation of signal response is possible for both surface and embedded induced flaws in part geometry specimens using a physics-based model. The proposed approach for NDE procedure qualification uses artificial flaws in simple geometry and part geometry specimens, and induced flaws in the same type of simple geometry specimens. Signal response data is taken on all sets of artificial and induced flaws in simple geometry and part geometry specimens. Moreover, simulated signal response data is generated for surface and embedded flaws. Thus, a case of five signal response versus flaw size datasets is considered. Three of the datasets are empirical and two datasets are physics model-based simulation datasets. A method of devising and using transfer function calculation dataset blocks to estimate the either the reliably detectable flaw size or the demonstration flaw size is provided.

Nondestructive evaluation↗
NASA NTRS1995

Efficient modeling of interconnects and capacitive discontinuities in high-speed digital circuits

Modeling of interconnects and associated discontinuities with the recent advances high-speed digital circuits has gained a considerable interest over the last decade although the theoretical bases for analyzing these structures were well-established as early as the 1960s. Ongoing research at the present time is focused on devising methods which can be applied to more general geometries than the ones considered in earlier days and, at the same time, improving the computational efficiency and accuracy of these methods. In this thesis, numerically efficient methods to compute the transmission line parameters of a multiconductor system and the equivalent capacitances of various strip discontinuities are presented based on the quasi-static approximation. The presented techniques are applicable to conductors embedded in an arbitrary number of dielectric layers with two possible locations of ground planes at the top and bottom of the dielectric layers. The cross-sections of conductors can be arbitrary as long as they can be described with polygons. An integral equation approach in conjunction with the collocation method is used in the presented methods. A closed-form Green's function is derived based on weighted real images thus avoiding nested infinite summations in the exact Green's function; therefore, this closed-form Green's function is numerically more efficient than the exact Green's function. All elements associated with the moment matrix are computed using the closed-form formulas. Various numerical examples are considered to verify the presented methods, and a comparison of the computed results with other published results showed good agreement.

Oh, K. S.↗
DOE OSTI2025

Attention-based functional-group coarse-graining: a deep learning framework for molecular prediction and design

Machine learning (ML) offers considerable promise for the design of new molecules and materials. In real-world applications, the design problem is often domain-specific, and suffers from insufficient data, particularly labeled data, for ML training. In this study, we report a data-efficient, deep-learning framework for molecular discovery that integrates a coarse-grained functional-group representation with a self-attention mechanism to capture intricate chemical interactions. Our approach exploits group-contribution concepts to create a graph-based intermediate representation of molecules, serving as a low-dimensional embedding that substantially reduces the data demands typically required for training. Using a self-attention mechanism to learn the subtle but highly relevant chemical context of functional groups, the method proposed here consistently outperforms existing approaches for predictions of multiple thermophysical properties. In a case study focused on adhesive polymer monomers, we train on a limited dataset comprising only 6,000 unlabeled and 600 labeled monomers. The resulting chemistry prediction model achieves over 92% accuracy in forecasting properties directly from SMILES strings, exceeding the performance of current state-of-the-art techniques. Furthermore, the latent molecular embedding is invertible, enabling the design pipeline to automatically generate new monomers from the learned chemical subspace. We illustrate this functionality by targeting several properties, including high and low glass transition temperatures (Tg), and demonstrate that our model can identify new candidates with values that surpass those in the training set. The ease with which the proposed framework navigates both chemical diversity and data scarcity offers a promising route to accelerate and broaden the search for functional materials.

Han, Ming [Univ. of Chicago, IL (United States)]↗
NASA NTRS1996

IRAS Colors of the Pleiades

We present large scale images of the infrared emission of the region around the Pleiades using the ISSA data product from the IRAS mission. Residual Zodiacal background and a discontinuity in the image due to the scanning strategy of the satellite necessitated special background subtraction methods. The 60/100 color image clearly shows the heating of the ambient interstellar medium by the cluster. The 12/100 and 25/100 images peak on the cluster as expected for exposure of small dust grains to an enhanced UV radiation field; however, the 25/100 color declines to below the average interstellar value at the periphery of the cluster. Potential causes of the color deficit are discussed. A new method of identifying dense molecular material through infrared emission properties is presented. The difference between the 100 micron flux density and the 60 micron flux density scaled by the average interstellar 60/100 color ratio (Delta I(sub 100) is a sensitive diagnostic of material with embedded heating sources (Delta I(sub 100) less than 0) and cold, dense cores (Delta I(sub 100) greater than 0). The dense cores of the Taurus cloud complex as well as Lynds 1457 are clearly identified by this method, while the IR bright but diffuse Pleiades molecular cloud is virtually indistinguishable from the nearby infrared cirrus.

Carey, Sean J.↗
DOE OSTI2024

Temporal and spatial characterization of a thermogenic, fault-controlled gas hydrate system, Woolsey Mound, Gulf of Mexico

Woolsey Mound, located at Mississippi Canyon Lease Block 118 (MC118), is the site of the Gulf of Mexico hydrate research consortium’s seafloor observatory, where gas hydrates outcrop at the seafloor. The presence of gas hydrates in the mound is confirmed directly by coring and indirectly by 3D seismic reflection data. Craters, pockmarks, chemosynthetic communities, and authigenic carbonates populate the seafloor at Woolsey Mound. Each crater is characterized by a network of shallow crestal faults that connect the hydrate mound to the underlying allochthonous salt body. We characterize the temporal and spatial evolution of gas hydrates at Woolsey Mound under natural perturbations using four collocated 3D seismic reflection data sets that span over 14 years. Data acquisition differences embedded in the data sets arising from variation in geometry, sample rate, and phase are minimized using the “cross-equalization” method. Our results indicate that hydrate formation and dissociation vary temporally and spatially in close connection to the shallow crestal faults. Evidence of gas hydrate dissociation is observed over a period of three years (2000–2003), where major dissociation occurred along the southern portion of the crestal fault in the southeast crater. The dissociation is less prominent in the southwest crater. Evidence of methane venting is observed between 2000 and 2010, which is mostly concentrated in the southeast crater. The residual amplitude anomalies observed between 2000 and 2014 in the mound are mostly positive, implying that the methane venting had increased significantly. The positive anomalies are correlated with the methane seepage recorded in 2011. Our results indicate the evolution of a fault-controlled gas hydrate system in the northern Gulf of Mexico, which would aid in assessing its impact on the seafloor.

Geochemistry & Geophysics↗
DOE OSTI2025

Pulsed Infrared Thermography Nondestructive Imaging of SiC-SiC f Composite Cladding Architectures; Understanding the Performance of SiC-SiC f Composite Cladding Architectures with Cr Coating in Normal Operating and Accident Conditions in LWRs and Advanced Reactors

SiC-SiC f composites, consisting of silicon carbide fibers embedded in a silicon carbide matrix, are advanced materials with high thermal conductivity, temperature stability, and resistance to radiation damage. Traditional methods for quality control of fabricated SiC-SiC f composites involve nondestructive evaluation (NDE) with X-ray computed tomography (XCT). However, XCT imaging of typical SiC-SiC f structures for cladding applications can involve several hours. In this project, we investigate an alternative approach to NDE of SiC-SiC f composites that involves rapid (on the order of seconds) imaging with Pulsed infrared thermography (PIT). PIT images of a planar SiC-SiC f specimen show the structure of the surface monolith layer and internal SiC f structures. The capability of PIT imaging in visualizing SiC f structures is qualitatively confirmed by observing similarity in the PIT and X-ray transmission images of the same specimen. Computer vision analysis of defects in the PIT image of the monolith was performed with thresholding followed by topological structural analysis that computed geometric descriptors, including major/minor axes of fitted ellipses, area, perimeter, Feret diameter, circularity, roundness, and solidity.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1981

Software error detection

Several methods were employed to detect both the occurrence and source of errors in the operational software of the AN/SLQ-32. A large embedded real time electronic warfare command and control system for the ROLM 1606 computer are presented. The ROLM computer provides information about invalid addressing, improper use of privileged instructions, stack overflows, and unimplemented instructions. Additionally, software techniques were developed to detect invalid jumps, indices out of range, infinte loops, stack underflows, and field size errors. Finally, data are saved to provide information about the status of the system when an error is detected. This information includes I/O buffers, interrupt counts, stack contents, and recently passed locations. The various errors detected, techniques to assist in debugging problems, and segment simulation on a nontarget computer are discussed. These error detection techniques were a major factor in the success of finding the primary cause of error in 98% of over 500 system dumps.

Buechler, W.↗
NASA NTRS1989

The morphology of strike-slip faults - Examples from the San Andreas Fault, California

The dilatational strains associated with vertical faults embedded in a horizontal plate are examined in the framework of fault kinematics and simple displacement boundary conditions. Using boundary element methods, a sequence of examples of dilatational strain fields associated with commonly occurring strike-slip fault zone features (bends, offsets, finite rupture lengths, and nonuniform slip distributions) is derived. The combinations of these strain fields are then used to examine the Parkfield region of the San Andreas fault system in central California.

Bilham, Roger↗
NASA NTRS1991

Identification of linear multivariable systems from a single set of data by identification of observers with assigned real eigenvalues

A formulation is presented for identification of linear multivariable from a single set of input-output data. The identification method is formulated with the mathematical framework of learning identifications, by extension of the repetition domain concept to include shifting time intervals. This method contrasts with existing learning approaches that require data from multiple experiments. In this method, the system input-output relationship is expressed in terms of an observer, which is made asymptotically stable by an embedded real eigenvalue assignment procedure. Through this relationship, the Markov parameters of the observer are identified. The Markov parameters of the actual system are recovered from those of the observer, and then used to obtain a state space model of the system by standard realization techniques. The basic mathematical formulation is derived, and numerical examples presented to illustrate.

Phan, Minh↗