MPpredictor: An Artificial Intelligence-Driven Web Tool for Composition-Based Material Property Prediction
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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
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Results for “materials property”
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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Effect of material properties on batch‐to‐glass conversion kinetics
Abstract A recently developed model of the cold cap—the reacting glass batch (melter feeds) floating on molten glass in an electric glass melter—couples heat transfer with the feed‐to‐glass conversion kinetics. The model allows for determining the distributions of temperature and various properties within the cold cap. In the present study, this model is applied to four melter feeds designed for high‐level and low‐activity nuclear wastes. Profiles of temperature, conversion degree, cold cap porosity and density, condensed matter velocity, and heating rate were determined using the material properties of the cold cap. Effects of vigorous foaming at the cold cap bottom were considered. Density, thermal conductivity, and glass production rate strongly affect the cold cap thickness and the fraction of undissolved silica entering the melt under the cold cap. The heating rate profile in the cold cap is highly nonlinear, with high heating rates observed in the foam layer.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
In situ observation of short- and long-timescale material property evolution under extreme conditions
Directly observing the evolution of material performance in-situ under extreme conditions remains a great challenge. Emergent behavior, such as the transition from incubation to steady state void swelling, is often difficult to capture in these conditions. To fill this gap, transient grating spectroscopy (TGS) has been used to collect multi-property information in-situ under high temperature exposure to ion beam irradiation. This all-optical method returns elastic and thermal transport properties with second-scale time resolution. Here, recent work using this methodology to track the evolution of Ni-based solid-solution alloys using the in situ ion irradiation TGS (I3TGS) beamline will be described. Tracking material property evolution during long exposure times (hours) provides a clear indication of when void swelling has occurred. On short timescales (seconds to minutes), observing rapid changes in thermoelastic properties as defect generation is initiated allows a unique window into bulk transient defect populations which are otherwise difficult to observe.
36 MATERIALS SCIENCE↗
Finite Element Modeling of Diffusion in Fractured Porous Media by Using Hierarchical Material Properties
Abstract Fractured porous media challenge modeling approaches due to high computational costs and excessive mesh refinement imposed by the extreme scale variability of fractures and the heterogeneity of the surrounding porous rock. To overcome such difficulties, we utilize the hierarchical finite element method ( Hi ‐FEM) that has been developed previously to simulate the electrical potential distribution in complex geologic environments. The method employs the hierarchical basis functions in classical finite element analysis to enable representation of material properties on each dimensional component of a given 3D unstructured finite element, thereby inherently allowing for interactions at the boundary between fracture and a host rock. In this study, we extend its application to transient fluid flow and heat conduction in the Laplace domain. Time‐domain flow solutions are obtained by numerical inverse Laplace transform. We evaluate the accuracy of the method using different flow models and demonstrate its robustness for large‐scale, rock mass models featuring complex fracture networks. Moreover, for the computation of nodal Darcian velocity fields in fractured porous media where the fractures are represented as 2D features, a new approach that employs the Yeh's Galerkin model for both volume and facet elements is proposed. Results show that Hi ‐FEM can produce accurate flow solutions for fractured porous media without any need of coupling or transfer mechanism while still being computationally economical and numerically robust, even for large‐scale simulations.
Beskardes, G. D.↗
Modeling the Impacts of Material Properties on Oscillatory Neuron Behavior
In this study, neuromorphic computing, which mimics the functions of biological brains, offers improvements in both latency and energy efficiency over typical von Neumann computing architectures. Spiking neural networks can be especially power-efficient because they encode information temporally and can use more sparse electrical inputs. Here, we study the design of volatile memristors (variable resistors with memory) for neuronal devices, with particular consideration toward the feasibility of all-on-chip oscillation using built-in capacitance. We use circuit simulations to model the behavior of oscillator neurons with a range of realistic material properties. We find that energy inputs increase with insulating-phase resistivity, thermal conductivity, and device aspect ratio. However, we also find that the minimum capacitance needed for oscillation decreases with increasing insulating-phase resistivity, which opposes the constraints for power efficiency. Based on published data on NbO 2 , VO 2 , and EuNiO 3 , we find that existing materials can be engineered for all-on-chip spiking using their parasitic capacitance.
36 MATERIALS SCIENCE↗
Dimorphos’s Material Properties and Estimates of Crater Size from the DART Impact
On 2022 September 26, the Double Asteroid Redirection Test (DART) spacecraft intentionally collided with Dimorphos, the moon of the binary asteroid system 65803 Didymos. This collision provided the first full-scale test of a kinetic impactor for planetary defense. Images from DART’s DRACO camera revealed Dimorphos to be an oblate spheroid covered in boulders of varying sizes and shapes. Very little was known about Dimorphos prior to DART’s impact, including its shape, structure, and material properties. Approach observations and those following the DART impact have provided crucial knowledge that narrows the parameter space relevant to modeling the impact into Dimorphos. Here we present the results of a suite of hydrocode simulations of the DART impact on Dimorphos. Despite remaining uncertainties, initial models of DART’s kinetic impact provide important information about the results of DART (e.g., potential crater size and morphology, ejecta mass) and the properties of Dimorphos. Simulations here suggest that Dimorphos has near-surface strength ranging from a few Pascals to tens of kPa, which corresponds to crater sizes of ~40–60 m. Simulated crater sizes provide a crucial comparison metric for the European Space Agency Hera mission when it arrives at the Didymos system. Hera’s measurement of crater size in combination with measurement of Dimorphos’s mass will allow us to assess our simulations and provide the information needed to make the DART impact experiment both the first test of a planetary defense mitigation mission and the first full-scale planetary defense simulation validation exercise.
36 MATERIALS SCIENCE↗
Physics-informed neural networks for identification of material properties using standing waves
A metallic structure in its initial stage of failure involves plastic deformation or environmental degradation that changes the elastic modulus and density. This work presents the detection of change in wave velocity (a function of elastic modulus and density) as a system identification problem. A physics-informed neural network (PINN) is proposed to solve the system identification problem. The PINN takes the spatial coordinates of scanning locations and time as inputs and provides the displacement and wave velocity as outputs. The governing partial differential equation of standing waves in a rod is incorporated into the neural network as physics in the form of a loss function. The wave velocity vector is randomly initiated. During the training of the network, physics is used to determine and update the wave velocity target vector from the network’s displacement predictions. The measured data, comprising sparse displacement response on the rod structure, are used to train the PINN. The wave velocity at the sparse locations on the rod is learned from the predicted displacements during the training. Using the predictions of the trained network, the response of free vibration or material property variation can be reconstructed at unscanned locations on the structure to obtain high-resolution maps for full-field imaging to detect and localize the changes caused by plastic deformation. The PINN’s sparse scanning and simultaneous prediction capability during training can lead to high scanning and data-processing speeds. This capability yields a nondestructive evaluation system that can predict the presence of degraded material locations as the structural vibrations are scanned and processed in real time.
Rathod, Vivek↗
A new method for obtaining model-free viscoelastic material properties from atomic force microscopy experiments using discrete integral transform techniques
Viscoelastic characterization of materials at the micro- and the nanoscale is commonly performed with the aid of force–distance relationships acquired using atomic force microscopy (AFM). The general strategy for existing methods is to fit the observed material behavior to specific viscoelastic models, such as generalized viscoelastic models or power-law rheology models, among others. Here we propose a new method to invert and obtain the viscoelastic properties of a material through the use of the Z-transform, without using a model. We present the rheological viscoelastic relations in their classical derivation and their z-domain correspondence. We illustrate the proposed technique on a model experiment involving a traditional ramp-shaped force–distance AFM curve, demonstrating good agreement between the viscoelastic characteristics extracted from the simulated experiment and the theoretical expectations. We also provide a path for calculating standard viscoelastic responses from the extracted material characteristics. The new technique based on the Z-transform is complementary to previous model-based viscoelastic analyses and can be advantageous with respect to Fourier techniques due to its generality. Additionally, it can handle the unbounded inputs traditionally used to acquire force–distance relationships in AFM, such as ramp functions, in which the cantilever position is displaced linearly with time for a finite period of time.
36 MATERIALS SCIENCE↗
Impact of Materials Properties on Higher-Temperature Engine Operation
Here, we examine the effects on materials temperatures and engine efficiency via simulations of engines operating at temperatures which exceed the thermal limits of today’s materials. Potential focus areas include high-speed, high-load operation (in the fuel-enrichment zone) as well as conditions of selective cooling at lower speeds and loads. We focus on a light-duty DISI and a heavy-duty CI engine using GT-Power. Temperature distributions within the head, block, piston, and valves were obtained from 3D FEA simulations coupled with 1D GT-Power representations of the engine’s gas flow and combustion regions. We use experimentally measured thermal properties of current commercial alloys for specific engine components, as well as candidate developmental alloys with improved temperature tolerance, to gauge the effects of materials properties on engine performance, particularly focusing on operating areas where materials which can withstand higher temperatures can enable intensified combustion conditions and increased specific output. The outcome of this work is guidance on materials selection targets and benefits as OEMs meet engine-performance needs in the coming decades, to include Class 8 line-haul freight vehicles with reduced emissions, as well as design of future near net zero-carbon light-duty range extenders for more rapid electrification of medium- and heavy-duty vehicles.
36 MATERIALS SCIENCE↗
Shear wave generation by mode conversion in picosecond ultrasonics: Impact of grain orientation and material properties
Abstract Picosecond ultrasonics has seen wide application in the investigation of interfaces in semiconductors and 3D imaging in biological systems. Shear ultrasonic waves are important for elastic constant and grain orientation measurement in ceramic materials. In this study, we investigate the impact of grain orientation and material's elastic properties on the generation efficiency of shear waves by mode conversion at a transducer thin film/substrate interface. The solution of acoustic wave equations suggests that crystal grain orientation has a strong impact on the generated shear wave amplitude. This dependence is found to be closely related to the magnitude of longitudinal component of wave displacements. The applicability of analytical model is validated by the experimental results from time‐domain Brillouin scattering. Moreover, material properties determine acoustic wave amplitudes based on the acoustic mismatch model and, particularly, large elastic anisotropy defined by Zener ratio favors strong shear waves. In the light of this analysis, several recommendations on suitable grain orientations and film materials are made to facilitate shear wave detection.
Wang, Yuzhou↗
Limits of Bulk-Material Property Calculations for X-Ray Opacity of Tungsten-Loaded Composite Materials, Project# 22-166 Year 1 of 1 (feasibility study)
Explore the source record for details and available documents.
SDRD↗
Limits of Bulk-Material Property Calculations for X-Ray Opacity of Tungsten-Loaded Composite Materials
Explore the source record for details and available documents.
36 MATERIALS SCIENCE↗
BACKFLIP Project: Determination of BACKsheet Material Properties: A Comparison of Market-Benchmark Technologies to Novel Non-Fluoro-Based Co-Extruded Backsheet Materials and Their Correlation and Impact on PV Module Degradation Rates
Quantifying degradation rate of backsheets will help procuring materials for 30+ years. Today most backsheets on the market have a laminated PET core; polyolefin materials may provide better properties for backsheets (barrier to water, mechanical properties through UV and hydrolytic environments, etc.). End-of-life regulations may require fluorine-free backsheet products in future global markets. Parametrized equation predicting the service life of backsheets, and validated by both lab and outdoor extensive testing. Technical and commercial confidence to the entire value chain: from backsheet and module manufacturers to the downstream community (IPP, banks, developers) that materials of higher quality can help reduce finance costs, therefore driving down LCOE. A fast track PV module market adaptation for novel high-quality materials, such as co-extruded polyolefin-core backsheets, by obtaining unbiased durability data.
14 SOLAR ENERGY↗
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↗
A Method for Producing Hierarchical and Statistically Calibrated Predictions of Nuclear Material Properties from Existing Models
Computer vision-based analysis of micrographs of nuclear materials is an emerging technique for property prediction, synthetic route identification, and other material analysis tasks. These analysis tasks play a pivotal role in many material characterization applications such as signature development for treaty verification, process optimization, etc. The backbone in many of the recent computer vision-based techniques is a deep learning model, which takes a fixed-size set of pixels and provides a class prediction for that set of pixels. For example, previous work developed a deep convolutional neural network (CNN) to predict the synthetic route from a 256 px x 256 px patch taken from a larger image of uranium ore concentrates. In this work, we present several methods for first calibrating these models in a manner that they can provide accurate probabilities of their predictions’ veracity, and several methods of combining these probabilities. Overall, the combination of these two steps into a pipeline allows for full-image and even full-sample (where a sample has many images) predictions with associated confidence values. Finally, we show that one can also use the patch predictions and confidence to produce a visualization to map predicted constituents through the image. Results and examples for predicting and mapping uranium ore concentrates’ synthetic process from imagery will be presented.
artificial intelligence↗
Material properties and mechanical behaviour of functionally graded steel produced by wire-arc additive manufacturing
Metal Big Area Additive Manufacturing is an additive manufacturing technique based on Gas Metal Arc Welding (GMAW) with the option to use many shielding gases, and materials. The system is equipped with a dual torch design allowing for printing different materials; in our study, AISI 410 stainless steel and AWS ER70S-6 mild steel are both printed in the same component. Different print strategies were designed to highlight changes in material and mechanical properties. Deformation behaviour of a materials’ interface was analysed by two-dimensional digital image correlation of uniaxial tensile specimens in displacement-controlled tests. Instances of non-homogeneous local strains adjacent to the interface are observed, as well as variability in mechanical behaviour and microstructure based on location within the print. Optical and electron microscopy are used to evaluate three microstructural zones in a 5 mm range of the interface between mild steel and stainless steel. Areas far from the interface produced polygonal ferrite and pearlite, while areas close to the interface produced acicular ferrite and bainite. Chromium redistribution profiles are dependent on the print strategy used, as shown by scanning electron microscopy with Energy dispersive spectroscopy. Evidence produced via electron backscatter diffraction is shown to support the argument that transformation induced plasticity is not the cause for the non-homogeneous deformation.
36 MATERIALS SCIENCE↗
A state-of-the-art review of experimental and computational studies of granular materials: Properties, advances, challenges, and future directions
Modeling of heterogeneous materials and media is a problem of fundamental importance to a wide class of phenomena and systems, ranging from condensed matter physics, soft materials, and composite media to porous media, biological systems, geosystems, ceramic engineering, pharmaceutical science and even in space discoveries. Among the most important materials are granular systems, which have received intense interest from the engineering, physics, and mathematics communities. In this review paper, the recent developments and new advances in experimental, and computational methods on a variety of scales and physics that extend understanding to a wide range of materials and phenomena are reviewed. Experimental advances include computed neutron and nanometer-scale tomography, magnetic resonance imaging, refractive index matching, digital image correlation, acoustic emission analysis, and the most recent 4D techniques. Furthermore, a tremendous shift has occurred from the continuum scale to micro-scale and developing multiscale approaches. As such, various computational methods, including, constitutive modeling, discrete modeling, and multiscale approaches, have been developed. In conclusion, aside from all these evolutions, more complicated modeling called coupled, or multiphysics, systems representing a simultaneous presence of heat, fluid, chemical variation, and mechanical effect are also explored.
36 MATERIALS SCIENCE↗