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PANES utilizes improved techniques for analysis of structures with material and geometric nonlinearities, including limit point and bifurcations behavior which occurs in buckling and collapse problems. Incremental loading, Newton-Raphson iteration, and higher order methods are used in program.
Thermal Energy Storage Units were developed for storing thermal energy required for operating Vuilleumier cryogenic space coolers. In the course of the development work the thermal characteristics of thermal energy storage material was investigated. By three distinctly different methods it was established that ternary salts did not release fusion energy as determined by ideality at the melting point of the eutectic salt. Phase change energy was released over a relatively wide range of temperature with a large change in volume. This strongly affects the amount of thermal energy that is available to the Vuilleumier cryogenic cooler at its operating temperature range and the amount of thermal energy that can be stored and released during a single storage cycle.
An optimization capability for involute structures has been developed. Its key feature is the use of global material geometry variables which are so chosen that all combinations of design variables within a set of lower and upper bounds correspond to manufacturable designs. A further advantage of global variables is that their number does not increase with increasing mesh density. The accuracy of the sensitivity derivatives has been verified both through finite difference tests and through the successful use of the derivatives by an optimizer. The state of the art in composite design today is still marked by point design algorithms linked together using ad hoc methods not directly related to a manufacturing procedure. The global design sensitivity approach presented here for involutes can be applied to filament wound shells and other composite constructions using material form features peculiar to each construction. The present involute optimization technology is being applied to the Space Shuttle SRM nozzle boot ring redesigns by PDA Engineering.
A method is presented for the calculation of elastic stresses in symmetrical disks typical of those of a high-temperature gas turbine. The method is essentially a finite-difference solution of the equilibrium and compatibility equations for elastic stresses in a symmetrical disk. Account can be taken of point-to-point variations in disk thickness, in temperature, in elastic modulus, in coefficient of thermal expansion, in material density, and in Poisson's ratio. No numerical integration or trial-and-error procedures are involved and the computations can be performed in rapid and routine fashion by nontechnical computers with little engineering supervision. Checks on problems for which exact mathematical solutions are known indicate that the method yields results of high accuracy. Illustrative examples are presented to show the manner of treating solid disks, disks with central holes, and disks constructed either of a single material or two or more welded materials. The effect of shrink fitting is taken into account by a very simple device.
In many large-scale scientific computations, it is necessary to use surface models based on information provided at only a finite number of points (rather than determined everywhere via an analytic formula). As an example, an equation of state (EOS) table may provide values of pressure as a function of temperature and density for a particular material. These values, while known quite accurately, are typically known only on a rectangular (but generally quite nonuniform) mesh in (T,d)-space. Thus interpolation methods are necessary to completely determine the EOS surface. The most primitive EOS interpolation scheme is bilinear interpolation. This has the advantages of depending only on local information, so that changes in data remote from a mesh element have no effect on the surface over the element, and of preserving shape information, such as monotonicity. Most scientific calculations, however, require greater smoothness. Standard higher-order interpolation schemes, such as Coons patches or bicubic splines, while providing the requisite smoothness, tend to produce surfaces that are not physically reasonable. This means that the interpolant may have bumps or wiggles that are not supported by the data. The mathematical quantification of ideas such as physically reasonable and visually pleasing is examined.
Point defects such as vacancies and impurity atoms strongly impact the performance of 2D materials. Traditional efforts often rely on manual detection, a process that is time-intensive, prone to human error, and challenging to scale. Here we leverage machine learning (ML) methods to identify and quantify vacancies within 2D transition metal carbides (Ti3C2, MXenes), aiming to expedite detection while improving accuracy. MXenes exhibit valuable defect-defined electrochemical properties, but we currently lack statistical understanding of defect topology needed to fully harness these materials. Here we employ a convolutional neural network for semantic segmentation of experimental MXene images, opening an opportunity to conduct a rigorous statistical study on defect hierarchy while investigating local relaxation in the lattice. We show how the integration of ML can yield fundamental insight into point defects, providing a powerful tool that will play an increasingly crucial role in the future of materials science. ML is often not just a matter of straightforward application, and pretrained models proved ineffective in this case. Instead, we trained our own neural network (NN) and applied data augmentation techniques and fine-tuning to the training dataset. Since labeled microscopy data is often scarce, we developed training data from a previously published wide-frame MXene image, using customized Gaussian fitting to locate atomic positions. Our trained model was then applied to a large dataset of experimental images, enabling a statistical study of defect configurations across three samples prepared with different HF etchant concentrations (5%, 9.1%, and 12.5%), as shown in Fig. 1. This also allowed us to investigate local strain around vacancies, though we find that we are limited by the precision of measurements using high-angle annular dark field (HAADF) images, as shown in Fig. 2. This study demonstrates how ML enables large-scale, quantitative analysis of atomic defects - an otherwise infeasible task with traditional methods. While our NN was specialized for Ti3C2 MXenes, the pipeline we developed provides a foundation for future ML models tailored to other materials. Ultimately, we envision embedding the NN onto the microscope to give real-time feedback to the user. To make this a reality, continued work is necessary to fully understand the NN's capabilities and limitations. This study gets one step closer to our goals of automated experimentation moving away from traditional methods of manual labeling. As ML capabilities advance, we hope to continue adapting and applying these techniques in microscopy.
The electrical characterization of materials, particularly superconductors, semiconductors, and those undergoing metal-insulator transitions (MITs), relies significantly on resistivity and Hall measurements as a function of temperature. The van der Pauw four-point probe method is commonly used for such measurements, which involves resistance measurements for different circuit configurations connected to sample contacts. However, repeating these measurements at different temperatures is challenging and time consuming. Here, this study introduces a novel approach utilizing a switching circuit controlled by an Arduino device and a LabVIEW program to automate resistance measurements for different electrical configurations. The effectiveness of this setup was demonstrated by testing V 2 O 3 thin film samples deposited on Al 2 O 3 (001) substrates using DC magnetron sputtering. The MIT temperature of the sample was 115 K during heating and 100 K during cooling. The sample exhibited p-type charge carriers with a Hall coefficient of 1.3 ± 0.1 x 10 -4 cm 3 /C and Hall mobility of 1.7 x 10 -1 cm 2 /V∙s, which is consistent with findings from other studies employing commercial equipment.
A unified viscoplastic constitutive relation based on crystallographic slip theory was developed for the deformation analysis of nickel base face centered cubic superalloy single crystals at elevated temperature. The single crystal theory is embedded in a self consistent method to derive a constitutive relation for a directionally solidified material comprised of a polycrystalline aggregate of columnar cylindrical grains. One of the crystallographic axes of the cylindrical crystals points in the columnar direction while the remaining crystallographic axes are oriented at random in the basal plane perpendicular to the columnar direction. These constitutive formulations are coded in FORTRAN for use in nonlinear finite element and boundary element programs.
A new method for solving problems in relative astrometry is proposed. In it, the relationship between the measured quantities and the components of the position vector of a star is modeled using quaternions, in effect replacing the plate constants of a standard four-plate-constant solution with the four components of a quaternion. The method allows a direct solution for the position vectors of the stars, and hence for the equatorial coordinates. Distortions, magnitude, and color effects are readily incorporated into the formalism, and the method is directly applicable to overlapping-plate problems. The advantages of the method include the simplicity of the resulting equations, their freedom from singularities, and the fact that trigonometric functions and tangential point transformations are not needed to model the plate material. A global solution over the entire sky is possible.
Point defects such as vacancies and impurity atoms strongly impact the performance of 2D materials. Traditional efforts often rely on manual detection, a process that is time-intensive, prone to human error, and challenging to scale. Here we leverage machine learning (ML) methods to identify and quantify vacancies within 2D transition metal carbides (Ti3C2, MXenes), aiming to expedite detection while improving accuracy. MXenes exhibit valuable defect-defined electrochemical properties, but we currently lack statistical understanding of defect topology needed to fully harness these materials. We employ a convolutional neural network for semantic segmentation of experimental MXene images, opening an opportunity to conduct a rigorous statistical study on defect hierarchy while investigating local relaxation in the lattice. We show how the integration of ML can yield fundamental insight into point defects, providing a powerful tool that will play an increasingly crucial role in the future of materials science.
Point defects such as vacancies and impurity atoms strongly impact the performance of 2D materials. Traditional efforts often rely on manual detection, a process that is time-intensive, prone to human error, and challenging to scale. Here we leverage machine learning (ML) methods to identify and quantify vacancies within 2D transition metal carbides (Ti3C2, MXenes), aiming to expedite detection while improving accuracy. MXenes exhibit valuable defect-defined electrochemical properties, but we currently lack statistical understanding of defect topology needed to fully harness these materials. Here we employ a convolutional neural network for semantic segmentation of experimental MXene images, opening an opportunity to conduct a rigorous statistical study on defect hierarchy while investigating local relaxation in the lattice. We show how the integration of ML can yield fundamental insight into point defects, providing a powerful tool that will play an increasingly crucial role in the future of materials science.
Report describes methods and materials used in waterproofing and fireproofing airplane fabrics using dopes. The determination of the probable life of a balloon fabric in service by experimental means is of great value in choosing the most suitable fabrics for a given purpose and in pointing the way to improvements in compounding and construction. The usefulness of exposure to the weather for this purpose has been amply demonstrated. Various attempts have been made to reproduce by artificial means the conditions promoting deterioration in service, but without marked success. Exposure to the weather remains the most satisfactory method for this purpose, and a consideration of the characteristics of such tests is therefore important. This report presents the results of a typical series of exposure tests made in 1917.
Although Metal matrix composites (MMCs) are superior to most sought-after metallic alloys, their challenging fabricability has limited their widespread use in bulk-form applications. Among the many advanced fabrication techniques, Additive Manufacturing (AM), owing to its unique capabilities to produce near-net shapes, has drawn significant traction in the past two decades, especially for materials that are difficult to process using traditional methods. However, unlike pure metal/alloy systems, MMCs are highly sensitive to the processing conditions prevailing in AM techniques due to factors such as the high melting point of reinforcement particles and the potential for in-situ reactions. Therefore, it may be a while before metal matrix composites are commercially produced via AM. This review will discuss the current state-of-the-art design, fabricability, and performance of various additively manufactured MMCs. A particular focus will be on microstructural evolution and microstructure-property relationships. The most employed AM techniques, such as directed energy deposition, powder bed fusion, binder jetting, sheet lamination, and solid-state friction stir processing, are fundamentally different in terms of thermo-kinetics, forming the perspective for this review. A detailed comparison of microstructural evolution and process parameter optimization, including feedstock preparation methods and the role of machine learning and modeling among the different AM processes, is also presented. Finally, a critical evaluation of emerging AM technologies for MMCs is also provided, highlighting their potential advantages and challenges.
Two dimensional and quasi-three dimensional, linear elastic finite element models for the prediction of crack growth characteristics, including crack growth direction, in laminated composite materials are presented. Mixed mode crack growth in isotropic materials, unidirectional and laminated composites is considered. The modified crack closure method is used to predict the applied load level for crack extension and two failure theories, modifications of the point stress and the Hashin failure criteria, are proposed to predict the direction of crack extension in composites. Comparisons are made with the Tsai-Wu failure criterion and the Sih strain energy density criterion as well as with experimental results. It is shown that the modified versions of point stress and Hashin criteria compare well with experiment.
The results of an experimental and analytical investigation of a biaxial tension/compression test for determining shear properties of composite materials are reported. Using finite element models of isotropic and orthotropic laminates, a specimen geometry was optimized. A kinematic fixture was designed to introduce an equal and opposite pair of forces into a specimen with a one inch square test section. Aluminum and several composite laminates with the optimized geometry and a configuration with large stress gradients were tested in the fixture. The specimens were instrumented with strain gages in the center of the test section for shear stiffness measurements. Pure shear strain was measured. The results from the experiments correlated well with finite element results. Failure of the specimens occurred through the center of the test section and appeared to have initiated at the high stress points. The results lead to the conclusion that the optimized specimen is suitable for determining shear modulus for composite materials. Further revisions to the specimen geometry are necessary if the method is to give shear strength data.
An overview of the fire toxicology program, its principal objectives and approach, is outlined. The laboratory methods of assessing pyrolysis product toxicity for two experiments are presented. The two experiments are: a comparison of test end points; and an evaluation of operant techniques. A third experiment is outlined for a comparison of full-scale and laboratory toxicity tests, with the purpose of determining animal survivability in full-scale tests. Future research plans are also outlined.
Three methods of photoelastic analysis are examined. The first method is an extension of the oblique incidence technique in which the model (or the light beam) is rotated about one of the material symmetry axes. In the second method, transmission and reflection photoelastic responses are combined. The third method requires the drilling of small holes and the determination of the fringe orders at selected points on the hole boundary. The three methods are applied to an orthotropic circular disk under diametral compression. Results are compared with strain gage data.
The question whether the galactic spiral arms are material objects or wave patterns is discussed. A semiempirical approach is adopted in presenting the concept of density waves. The theory of density waves is considered, giving attention to a survey of theoretical developments by analytical methods, the implication of a spiral pattern of density waves, spirals with moderately small pitch angle, and the origin and permanence of galactic spirals. The theoretical aspects discussed are tested against more detailed observations in the Milky Way system. It is pointed out that the density wave concept introduced by Lindblad, including the material concentration of both gas and stars, is the essential basis for the spiral structure of disk-shaped galaxies.