Considerations for the Identification of Elasto-Plastic Material Model Parameters
Explore the source record for details and available documents.
Fayad, Samuel Saiid↗
SEARCH · Engineering Papers
Results for “material identification”
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Considerations for the Identification of Elasto-Plastic Material Model Parameters
Explore the source record for details and available documents.
Fayad, Samuel Saiid↗
Optimizing Classifiers for Radionuclide Identification
Identifying threat nuclear materials is a critical for the prevention of acts of nuclear terrorism on the homeland. For this purpose, many radionuclide identification devices are deployed in the field. However, these will not necessarily be in the hands of non-experts, therefore these devices need to provide ready-made answers for the personnel in the field. This is where advanced algorithms are employed to both interpret the data and provide the identification of the nuclear material being interrogated. We took a machine learning approach to identification, by using training and validation data sets to create and optimize classifiers which determine which radionuclide is consistent with the data. The classifiers investigated were the Random Forest, Decision Tree, Support Vector Machine, and XG Boost and their performance was judged using the F1 Score for both hyperparameter tuning and comparison. In the end, we found out that the Random Forest Classifier worked the best based off the F1 Score they got which was 0.98.
45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
National Nuclear Materials Archive (NNMA) - Specimen Selection and Sampling at the Y-12 National Security Complex
Y-12 has made significant progress in FY21 towards National Nuclear Materials Archive (NNMA) program objectives related to the identification, nomination, sampling, sub-sampling and shipment of NNMA materials. Y-12 efforts in FY22 and beyond are anticipated to transition away from material identification and nomination to an emphasis on sampling/sub-sampling and long-term storage of NNMA specimens. Long-term storage efforts will also involve more emphasis on insuring that the nuclear forensics value of NNMA items is guaranteed through appropriate preservation processes.
36 MATERIALS SCIENCE↗
Low thermal conductivity in Bi 8 CsO 8 SeX 7 (X = Cl, Br) by combining different structural motifs
Understanding the structure–property relationships of materials in order to supress thermal conductivity is crucial for developing efficient thermoelectric generators and thermal barrier coatings. Low thermal conductivity materials can often contain a single dominant phonon scattering mechanism. Here, we highlight how combining different structural features into one material can aid in the design and identification of new materials with low thermal conductivities. We synthesise two new mixed-anion materials, Bi 8 CsO 8 SeX 7 (X = Cl and Br), with low thermal conductivities of 0.27(2) and 0.22(2) W m -1 K -1 respectively, measured along their c-axes at room temperature. The Bi 8 CsO 8 SeX 7 materials possess a combination of bond strength hierarchies, Cs + vacancies, and low frequency Cs + rattling. These different features significantly inhibit phonon transport along different crystallographic directions. Due to sharp bond strength contrast between the van der Waals gaps and [Bi 2 O 2 ] 2+ layers, the Bi 8 CsO 8 SeX 7 materials exhibit thermal conductivities <50% of the theoretical minimum when measured along the stacking direction. Conversely, the thermal conductivity associated with the ab-plane is reduced by Cs + rattling when compared to the structurally and compositionally related BiOCl.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Comparative Study on Deep Learning Methods for Defect Identification and Classification in Composite Aerostructure Material
Aircraft structures are required to have a high level of quality to satisfy their need for light weight, efficient flight, and withstanding high loads over their lifespan. These aerostructures are typically made from composite material due to their good tensile strength and resistance to compression. To ensure their structural integrity, the composite material requires inspection for common flaws such as porosity, delaminations, voids, foreign object debris, and other defects. Ultrasonic testing (UT) is a popular non-destructive inspection (NDI) technique used for effectively evaluating composite material. Current inspection methods rely heavily on human experience and are extremely time consuming. Therefore, there is a need for the development of techniques to reduce the manual inspection time. This work compares the performance of different deep learning-based methods in the identification and classification of defects. Deep learning has shown great promise in numerous fields, and we show its effectiveness in the evaluation of composite aerostructure material. Furthermore, the methods developed here are both highly reliable with a top recall value of 98.63% as well as extremely efficient requiring an average of 4 seconds during the inferencing stage to evaluate new composites.
composites↗
Controllable topological insulator phases in litharge-phase InBi monolayer
Despite recent advances of layered square-net topological material models that possess ideal semimetallic electronic structures and promising potential in material applications, the identification of experimentally accessible two-dimensional square-net materials with related topological properties has proven challenging. Due to the highly tunable physical and topological properties of III-V semiconductors, we revisit the class of III-V materials and observe that the litharge-phase InBi is a layered square-net material and can be exfoliated into the InBi monolayer. We present a comprehensive first-principles study of the energy landscape of the InBi monolayer. We identify a paraelastic phase and three ferroelastic phases and study their topological properties. Specifically, we show that the paraelastic InBi monolayer is a trivial insulator due to the orbital-ordering-induced band inversion occurring between states with the same parity. Substituting one Bi atom per cell with another V-group element (N, P, As) or applying an electric field that breaks the inversion symmetry and changes the orbital onsite energy, the paraelastic InBi monolayer can be driven into the topological insulator phase. Furthermore, one of the ferroelastic phases of pure InBi, which can be obtained by gently straining the paraelastic phase, also possesses such topological insulating properties. Furthermore, these results provide several experimentally accessible routes to tune the nontrivial topology in the InBi monolayer, including creating heterostructures with piezoelectric or ferroelectric substrates and applying mechanical strain, making the InBi monolayer an ideal platform to study the interplay of reduced dimensionality, square-net chemical bonding networks, and band topology.
2-dimensional systems↗
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↗
Probing Surface/Bulk Structural Chemistry of Key Components of Solid Oxide Electrochemical Cells with In Situ / Operando Raman Spectroscopy
The remarkable attributes of solid oxide electrochemical cell technology (e.g., energy efficiency, low cost, scalability, low emissions, and operational flexibility, etc.) drive the wider adoption of electrochemical conversion routes for sustainability. It is critical for the codevelopment of solid oxide cell materials and processes to establish the mechanistic understanding of the underlying chemical phenomena at the molecular level. Herein, we summarize the advancements in Raman spectroscopy that provide structural/molecular information on electrode/electrolyte materials typically used in solid oxide cells for energy conversion. In particular, we discuss the multifactorial environment induced chemical processes that govern the performance and longevity of solid oxide electrochemical devices. The in situ/operando Raman spectroscopic investigations on the electrode/electrolyte materials reported in the literature are summarized with the emphasis on identification of key material properties that control the functional aspects of the solid oxide cells. The molecular level understanding of the electrochemical processes will allow advancement of the rational design of electrochemical materials for process level deployment of solid oxide cell technology.
Electrodes↗
Identification and Nomination of National Nuclear Materials Archive Specimens at the Oak Ridge National Laboratory
Explore the source record for details and available documents.
Robinson, Sharon↗
Development of a Multi-Sensor Data Science System Used for Signature Development on Solvent Extraction Processes in support of safeguards- an overview
A new nuclear fuel cycle test bed is being built at Idaho National Laboratory to support the purification of special nuclear material recovered from used fuel. The test bed provides an opportunity to research process flow and the application of computational tools in solvent extraction processes. A deeper understanding of process and equipment behavior coupled with real time data collection can indicate whether a process failure is accidental or purposeful. The goal of this project is to develop a system that utilizes non-traditional measurement sources such as vibration, acoustics, current, light, flow, and temperature in conjunction with data-based, machine learning techniques that will allow for signal discovery. This multi-sensor data can support the development of safeguards by design and security by design measures for such a facility. Additionally, it can aid in early detection and identification of removed materials indicating diversion, which is essential for initiating material recovery and actor identification. This overview encompasses the current research and testing of sensors to develop a spectrum of process signatures. To be followed by planned experiments aimed to characterize said signatures and study potential feature extraction techniques to identify a fault in the system (i.e. flow diversion).
98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
Tardigrade: Summary (LL20-AM-sensor)
Tardigrade is an effort to create small, passive remote sensor for material detection and identification. The microbolometer based sensor design uses custom bandpass filter bands combined with tuned thermal expansion materials in a ruggedized lens housing to provide passive mechanical correction for optical changes across a wide temperature range. This requires developing computational tools to model and predict thermal performance of meta-materials composed of multiple structured base materials and void-space, along with matching and developing 3D print technology to demonstrate the housing. Current efforts in the project are in 3 areas: material design algorithm development (transitioning into full 3D material design space), multi-material additive manufacturing process exploration, and sensor/optics design and engineering.
42 ENGINEERING↗
Random Forest Optimization for Radionuclide Identification
Radionuclide identification through gamma-ray spectroscopy is an indispensable tool in combatting the illicit smuggling of nuclear material. The radionuclide identification devices used in the field need to provide ready-made answers to non-experts, and therefore require sophisticated algorithms that can interpret the underlying data. We investigated the Random Forest classifier as a tool for identifying the radionuclide that is consistent with the data. We were provided with training and validations data sets and used them to optimize the two hyperparameters of the classifiers: maximum features required, and minimum samples used to split each node. The F1 score, a harmonic mean of precision and recall, was used to evaluate the performance of each built classifier. We found the optimal performance with minimum samples of 5 and maximum features of 50, with the F1 score of 0.95.
45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
Influence of temperature on accessible pyrolysis pathways of homopolymerized bisphenol A/F epoxies and copolymers
Understanding the thermal pyrolysis of epoxies and their copolymers is important for identifying structural changes resultant from thermal transients, enabling identification of failure modes of high-performance composite materials. This work expands our understanding of the thermal pyrolysis of cured epoxies and the role of temperature and composition on products, pathways, and relative rates. Numerous researchers have explored the pyrolysis of bisphenol A (BPA) epoxy. Significantly fewer have studied bisphenol F (BPF), and copolymers of BPA and BPF have been neglected. In this work, a pyrolysis gas chromatography mass spectrometer (PY-GC-MS) was used to investigate the degradation mechanism of homopolymerized BPA, BPF epoxies and their copolymers. Additionally, for polymer identification, pyrolysis >450 °C resulted in the most extensive fragmentation and is useful for material identification, though lower temperatures show different degradation product profiles that provide mechanistic insight into thermal degradation pathways. Temperature greatly influences the accessible pyrolysis pathways of BPA, revealing dual mechanisms of formation for products p-isopropylphenol and p-isopropenylphenol. For BPF at low pyrolysis temperature the p,p-bisphenol F isomer is produced at significantly lower relative abundance compared to the abundance at higher temperatures tested, but production of the other two isomers changes little with respect to temperature. This suggests the epoxy components consisting of the p,p-bisphenol F isomer have higher thermal stability. Overall, the copolymer epoxies were found to have similar degradation products in stoichiometric distributions. The major exception was the formation of the p,p-bisphenol F isomer, which shows evidence of thermal stabilizing effects from addition of BPA epoxy.
36 MATERIALS SCIENCE↗
Evaluating Model Robustness for Defect Identification and Classification in a Composite Aerostructure Material
Aircraft structures are required to have a high level of quality to satisfy their need for light weight, efficient flight, and withstanding high loads over their lifespan. These aerostructures are typically made from a composite material due to their good tensile strength and resistance to compression. To ensure their structural integrity, the composite material requires inspection for common flaws such as porosity, delaminations, voids, foreign object debris, and other defects. Ultrasonic testing (UT) is a popular non-destructive inspection (NDI) technique used for effectively evaluating the composite material. Current inspection methods rely heavily on human experience and are extremely time consuming. Therefore, there is a need for the development of techniques to reduce the manual inspection time. This work compares the performance of different deep learning-based methods in the identification and classification of defects. Deep learning has shown great promise in numerous fields, and we show its effectiveness in the evaluation of the composite aerostructure material. The methods developed here are both highly reliable with a top recall value of 98.64% as well as extremely efficient requiring an average of 4 s during the inferencing stage to evaluate new composites. Lastly, we investigate model robustness to concept drift by measuring its performance over time.
36 MATERIALS SCIENCE↗
Carbonyl Identification and Quantification Uncertainties for Oxidative Polymer Degradation
The most revealing indicator for oxidative processes or state of degraded plastics is usually carbonyl formation, a key step in materials degradation as part of the carbon cycle for man-made materials. Hence, the identification and quantification of carbonyl species with infrared spectroscopy have been the method of choice for generations, thanks to their strong absorbance and being an essential intermediate in carbon oxidation pathways. Despite their importance, precise identification and quantification can be challenging and rigorous fully traceable data are surprisingly rare in the existing literature. An overview of the complexity of carbonyl quantification is presented by the screening of reference compounds in solution with transmission and polymer films with ATR IR spectroscopy, and systematic data analyses. Significant variances in existing data and their past use have been recognized. Guidance is offered how better measurements and data reporting could be accomplished. Experimental variances depend on the combination of uncertainty in exact carbonyl species, extinction coefficient, contributions from neighboring convoluting peaks, matrix interaction phenomena and instrumental variations in primary IR spectral acquisition (refractive index and penetration depth for ATR measurements). In addition, diverging sources for relevant extinction coefficients may exist, based on original spectral acquisition. For common polymer degradation challenges, a relative comparison of carbonyl yields for a material is easily accessible, but quantification for other purposes, such as degradation rates and spatially dependent interpretation, requires thorough experimental validation. All variables featured in this overview demonstrate the significant error margins in carbonyl quantification, with exact carbonyl species and extinction coefficients already being major contributors on their own.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Electronic and topological properties of the van der Waals layered superconductor PtTe
We report the crystal growth and structural and electronic properties of superconducting, van der Waals layered PtTe. Easily cleavable crystals with a platelike morphology consistent with the layered structure were grown from a platinum-rich flux. A consistent determination of T c = 0.57 K is made from the onset of diamagnetism, the zero of resistivity, and the midpoint of the heat-capacity jump. The observed behavior is consistent with type-II superconductivity, with upper critical field at T = 0 estimated using the Werthamer-Helfand-Hohenberg theory to be 143 and 65 Oe for fields out of and in the plane, respectively. The heat-capacity discontinuity is close to the weak-coupling BCS value. Density-functional theory calculations and analysis of the electronic structure finds that PtTe is a topological semimetal with numerous surface states but suggests that the superconducting state itself may be topologically trivial. Additionally, angle-resolved photoemission spectroscopy reveals a normal-state Fermi surface in remarkable agreement with theory and confirms the overall topological nature of the material by experimental identification of the surface bands. Together, these findings identify PtTe as an interesting example of a cleavable, topological, and superconducting material.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Complex Functional Materials
The Complex Functional Materials (CFM) area of leadership focuses on the identification and development of materials comprised of multiple components or building blocks that are integrated or chemically bound together to achieve a desired function or response. Soft materials, such as polymers, as well as structural properties at the meso- and micro-scale that control materials function are particularly emphasized in this leadership area. Further, complex functional materials are generally developed to satisfy multiple materials design criteria all of which are essential to the overall application. CFM supports applications that are aligned with all three Los Alamos National Laboratory mission areas. These mission areas are: materials for nuclear deterrence, materials for energy security, and materials for global security
36 MATERIALS SCIENCE↗