Fracturing Fluid-Induced Mineralogy Changes and Impact on Elastic Properties for the Caney Shale, Oklahoma
Fracturing Fluid-Induced Mineralogy Changes and Impact on Elastic Properties for the Caney Shale, Oklahoma
Awejori, Gabriel↗
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Ultrasonic and micromechanical study of damage and elastic properties of SiC/RBSN ceramic composites
Ultrasonic techniques are employed to develop methods for nondestructive evaluation of elastic properties and damage in SiC/RBSN composites. To incorporate imperfect boundary conditions between fibers and matrix into a micromechanical model, a model of fibers having effective anisotropic properties is introduced. By inverting Hashin's (1979) microstructural model for a composite material with microscopic constituents the effective fiber properties were found from ultrasonic measurements. Ultrasonic measurements indicate that damage due to thermal shock is located near the surface, so the surface wave is most appropriate for estimation of the ultimate strength reduction and critical temperature of thermal shock. It is concluded that bonding between laminates of SiC/RBSN composites is severely weakened by thermal oxidation. Generally, nondestructive evaluation of thermal oxidation effects and thermal shock shows good correlation with measurements previously performed by destructive methods.
Chu, Y. C.↗
Vibrational, Elastic Properties and Sound Velocities of MgTi{sub 2}O{sub 4} Spinel
Zone-centre phonon frequencies, elastic properties and sound velocities along high-symmetry directions and Debye temperature of MgTi{sub 2}O{sub 4} spinel is calculated using rigid ion model. The interatomic interactions up to third nearest neighbours are also calculated. The main results of the present paper reveals that the tetrahedral bonding is stronger than the octahedral bonding. Calculated results are compared with previously available experimental and theoretical data. It is observed that the obtained results are in good agreement with the available results in literature.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Effects of temperature distribution and elastic properties of materials on gas-turbine-disk stresses
Calculations were made to determine the influence of changes in temperature distribution and in elastic material properties on calculated elastic stresses for a typical gas-turbine disk. Severe temperature gradients caused thermal stresses of sufficient magnitude to reduce the operating safety of the disk. Small temperature gradients were found to be desirable because they produced thermal stresses that subtracted from the centrifugal stresses in the region of the rim. The thermal gradients produced a tendency for a severe stress condition to exist near the rim but this stress condition could be shifted away from the region of blade attachment by altering the temperature distribution. The investigation of elastic material properties showed that centrifugal stresses are slightly affected by changes in modulus of elasticity, but that thermal stresses are approximately proportional to modulus of elasticity and to coefficient of thermal expansion.
Holms, Arthur G↗
Elastic properties of a Sc–Zr–Nb–Ta–Rh–Pd high-entropy alloy superconductor
Here, we report a comprehensive study on the elastic properties of a hexanary high-entropy alloy superconductor (ScZrNbTa) 0.685 [RhPd] 0.315 at room and cryogenic temperatures, by Resonant Ultrasound Spectroscopy experiments. The derived elastic constants are bulk modulus K=132.7 GPa, Young’s modulus E=121.0 GPa, shear modulus G=44.9 GPa, and Poisson’s ratio v= 0.348 for room temperature. The Young’s and shear moduli are ~ 10% larger than those in NbTi superconductor with similar T c , while the ductility is comparable. Moreover, the mechanical performance is further enhanced at cryogenic temperature. Our work confirms the advantageous mechanical properties of high-entropy alloy superconductors and suggests the application prospects.
36 MATERIALS SCIENCE↗
Status Report: Design and Fabricate Installation-Ready Test Capsule for the In-Situ Measurement of Elastic Properties Using an Optical Fiber-Based Measurement Technique
Microstructure evolution due to irradiation in a nuclear reactor can have a dramatic effect on material properties. A better understanding of this evolution is necessary for developing improved nuclear fuels and materials. The ability to measure such changes in real time is extremely limited due to the harsh conditions, high radiation fields, and limited access of the reactor environment. Through carefully designed experiments, measurement of elastic properties can be tied directly to microstructure. We present the methodology, design, and deployment plan for an instrument that has been developed to monitor grain microstructural changes during irradiation. Our measurement approach involves exciting and measuring the resonant frequency of a thin cantilever beam. Excitation and detection of the flexural vibrations of the beam are accomplished using optical methods that require only an optical fiber connection between the instrumentation and the sample. This technique has been demonstrated in a laboratory setting to monitor the recrystallization of highly textured copper during high temperature annealing. A test capsule incorporating this technique has been developed for in reactor testing. The capsule has been designed to be compatible with a reusable test module that allows simplified insertion in the TREAT reactor at INL. An exact replica of the instrument described in this Status Report, constructed from Quality Level 3 materials, is slated to be inserted into the TREAT reactor in FY19.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Probing the Effect of Hydrogen on Elastic Properties and Plastic Deformation in Nickel Using Nanoindentation and Ultrasonic Methods
Hydrogen effects on small-volume plasticity and elastic stiffness constants are investigated with nanoindentation of Ni-201 and sonic velocity measurements of bulk Ni single crystals. Elastic modulus of Ni-201, calculated from indentation data, decreases ~22% after hydrogen charging. This substantial decrease is independently confirmed by sonic velocity measurements of Ni single crystals; c 44 decreases ~20% after hydrogen exposure. Furthermore, clear hydrogen-deformation interactions are observed. The maximum shear stress required to nucleate dislocations in hydrogen-charged Ni-201 is markedly lower than in as-annealed material, driven by hydrogen-reduced shear modulus. Additionally, a larger number of depth excursions are detected prior to general yielding in hydrogen-charged material, suggesting cross-slip restriction. Together, these data reveal direct correlation between hydrogen-affected elastic properties and plastic deformation in Ni alloys.
36 MATERIALS SCIENCE↗
NDE Elastic Properties of Fiber-Reinforced Composite Materials
Fiber-reinforced composites are increasingly replacing metallic alloys as structural materials for primary components of fracture-critical structures. This trend is a result of the growing understanding of material behavior and recognition of the desirable properties of composites. A research program was conducted on NDE methods for determining the elastic properties of composites.
Composites NDE ultrasonic plate waves laminates↗
Predicting Elastic Properties of Materials from Electronic Charge Density Using 3D Deep Convolutional Neural Networks
Materials representation plays a key role in machine learning-based prediction of materials properties and new materials discovery. Currently both graph and three-dimensional (3D) voxel representation methods are based on the heterogeneous elements of the crystal structures. Here, we propose to use electronic charge density (ECD) as a generic unified 3D descriptor for materials property prediction with the advantage of possessing close relation with the physical and chemical properties of materials. We developed an ECD-based 3D convolutional neural networks (CNNs) for predicting the elastic properties of materials, in which CNNs can learn effective hierarchical features with multiple convolving and pooling operations. Extensive benchmark experiments over 2170 $Fm\bar3m$ face-centered-cubic materials show that our ECD-based CNNs can achieve good performance for elasticity prediction. Especially, our CNN models based on the fusion of elemental Materials-Agnostic Platform for Informatics and Exploration features and ECD descriptors achieved the best fivefold cross-validation performance. More importantly, we showed that our ECD-based CNN models can achieve significantly better extrapolation performance when evaluated over nonredundant data sets, where there are few neighbor-training samples around test samples. As an additional validation, we evaluated the predictive performance of our models on 329 materials of space group $Fm\bar3m$ by comparing to density functional theory calculated values, which shows a better prediction power of our model for bulk modulus than shear modulus. Because of the unified representation power of ECD, it is expected that our ECD-based CNN approach can also be applied to predict other physical and chemical properties of crystalline materials.
36 MATERIALS SCIENCE↗
Elastic properties of CaO under pressure and temperature.
Polycrystalline CaO sound wave velocities and pressure and temperature derivatives, Gruneisen parameter and other elastic properties
Soga, N.↗
Computer program for investigating effects of nonlinear suspension-system elastic properties on parachute inflation loads and motions
A computer program is presented by which the effects of nonlinear suspension-system elastic characteristics on parachute inflation loads and motions can be investigated. A mathematical elastic model of suspension-system geometry is coupled to the planar equations of motion of a general vehicle and canopy. Canopy geometry and aerodynamic drag characteristics and suspension-system elastic properties are tabular inputs. The equations of motion are numerically integrated by use of an equivalent fifth-order Runge-Kutta technique.
Poole, L. R.↗
Pressure induced phase transition and elastic properties of cerium mono-nitride (CeN)
In the present paper, we have investigated the high-pressure structural phase transition and elastic properties of cerium mono-nitride. We studied theoretically the structural properties of this compound (CeN) by using the improved interaction potential model (IIPM) approach. This compound exhibits first order crystallographic phase transition from NaCl (B{sub 1}) to tetragonal (BCT) phase at 37 GPa. The phase transition pressures and associated volume collapse obtained from present potential model (IIPM) show a good agreement with available theoretical data.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
A first principle study of the pressure dependent elastic properties of monazite LaPO{sub 4}
DFT based ab-initio simulations have been performed to study the effect of pressure on the elastic properties of monazite LaPO{sub 4} which is a promising host material for immobilization of high level nuclear waste. The phase is found to be stable up to 30 GPa. The calculated polycrystalline bulk, shear and Young moduli show an increasing trend as a function of pressure. The ductility and anisotropy in shear modulus of the material have been found to increase with pressure; whilethe bulk modulus anisotropy decreases with pressure.
36 MATERIALS SCIENCE↗
Efficient machine-learning model for fast assessment of elastic properties of high-entropy alloys
We combined descriptor-based analytical models for stiffness-matrix and elastic-moduli with mean-field methods to accelerate assessment of technologically useful properties of high-entropy alloys, such as strength and ductility. Model training for elastic properties uses Sure-Independence Screening (SIS) and Sparsifying Operator (SO) method yielding an optimal analytical model, constructed with meaningful atomic features to predict target properties. Computationally inexpensive analytical descriptors were trained using a database of elastic properties determined from density functional theory for binary and ternary subsets of Nb-Mo-Ta-W-V refractory alloys. The optimal Elastic-SISSO models, extracted from an exponentially large feature space, give an extremely accurate prediction of target properties, similar to or better than other models, with some verified from existing experiments. Here we also show that electronegativity variance and elastic-moduli can directly predict trends in ductility and yield strength of refractory HEAs, and reveals promising alloy concentration regions.
36 MATERIALS SCIENCE↗
Structural, electronic and elastic properties of RERu{sub 2} (RE=Pr and Nd) Laves phase intermetallic compounds
We have performed the first-principles calculations to study the structural, electronic and elastic properties of RERu{sub 2} (RE = Pr and Nd) Laves phase intermetallic compounds using full-potential linearized augmented plane wave (FP-LAPW) method based on density functional theory (DFT) within the generalized gradient approximation (GGA) for exchange and correlation potential. The optimized lattices constant are in reasonable agreement with available experimental data. The electronic properties are analyzed in terms of band structures, total and partial density of states, which confirm their metallic character. The calculated elastic constants infer that these compounds are mechanically stable in C15 (MgCu{sub 2} type) structure and found to be ductile in nature.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Elastic properties of a micro-breccia, igneous rock and lunar fines from Apollo 11 mission
Micro-breccia, igneous rocks and lunar fines elastic properties at ambient conditions and as function of pressure, discussing near surface mare region models
Anderson, O. L.↗
An AI-driven microstructure optimization framework for elastic properties of titanium beyond cubic crystal systems
Materials design aims to identify the material features that provide optimal properties for various engineering applications, such as aerospace, automotive, and naval. One of the important but challenging problems for materials design is to discover multiple polycrystalline microstructures with optimal properties. This paper proposes an end-to-end artificial intelligence (AI)-driven microstructure optimization framework for elastic properties of materials. In this work, the microstructure is represented by the Orientation Distribution Function (ODF) that determines the volume densities of crystallographic orientations. The framework was evaluated on two crystal systems, cubic and hexagonal, for Titanium (Ti) in Joint Automated Repository for Various Integrated Simulations (JARVIS) database and is expected to be widely applicable for materials with multiple crystal systems. The proposed framework can discover multiple polycrystalline microstructures without compromising the optimal property values and saving significant computational time.
36 MATERIALS SCIENCE↗
Learning interpretable surface elasticity properties from bulk properties via neural network equation learners
Surface elasticity is central to understanding the mechanics and stability of surfaces and interfaces. It is characterized by quantities such as surface tension, residual surface stress, and surface stiffness. However their analytical expressions are typically difficult to derive from atomistic data, and depend strongly on modeling choices. This work presents a neural network-based equation learner which combines customized activation functions and connection-based pruning to discover parsimonious, closed-form equations for surface elasticity from atomistic simulations. Applying the method to seven face-centered cubic (FCC) metals, our equation learner uncovers interpretable equations that describe both low-Miller index and high-Miller index surface properties, capturing long-tail property distributions accurately. The discovered expressions are decoupled into two components: a universal, geometry-driven orientation function, and material-specific baseline coefficients. We find that lower-order properties such as surface tension are fundamentally geometry dependent, while higher-order properties such as surface stress and elasticity show more complex geometry and material dependence. We also relate material dependent coefficients to bulk properties, forming a clear map from bulk material properties to surface elasticity. Overall, this approach demonstrates that interpretable neurosymbolic machine learning can bridge the gap between atomistic simulations and physical laws, enabling the discovery of generalizable structure–property relationships for materials science phenomena such as surface elasticity.
Equation learning↗