An Overview of Nuclear Thermal Rocket Element Environmental Simulator (NTREES) Capabilities, Upgrades, and Overlaps in Advanced Material Testing Areas
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NTREES↗
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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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Anomalous Behavior of 2D Janus Excitonic Layers under Extreme Pressures
Newly discovered 2D Janus transition metal dichalcogenides layers have gained much attention from a theory perspective owing to their unique atomic structure and exotic materials properties, but little to no experimental data are available on these materials. Here, experimental and theoretical studies establish the vibrational and optical behavior of 2D Janus S–W–Se and S–Mo–Se monolayers under high pressures for the first time. Chemical vapor deposition (CVD)-grown classical transition metal dichalcogenides (TMD) monolayers are first transferred onto van der Waals (vdW) mica substrates and converted to 2D Janus sheets by surface plasma technique, and then integrated into a 500 µm size diamond anvil cell for high-pressure studies. Overall, the results show that 2D Janus layers do not undergo phase transition up to 15 GPa, and in this pressure regime, their vibrational modes exhibit a nonmonotonic response to the applied pressures (dω/dP). Interestingly, these 2D Janus monolayers exhibit unique blueshift in photoluminescence (PL) upon compression, which is in contrast to many other traditional semiconductor materials. Overall theoretical simulations offer in-depth insights and reveal that the overall optical response is a result of competition between the ab-plane (blueshift) and c-axis (redshift) compression. The overall findings shed the very first light on how 2D Janus monolayers respond under extreme pressures and expand the fundamental understanding of these materials.
2D Janus monolayers↗
Utility fog: A universal physical substance
Active, polymorphic material ('Utility Fog') can be designed as a conglomeration of 100-micron robotic cells ('foglets'). Such robots could be built with the techniques of molecular nanotechnology. Controllers with processing capabilities of 1000 MIPS per cubic micron, and electric motors with power densities of one milliwatt per cubic micron are assumed. Utility Fog should be capable of simulating most everyday materials, dynamically changing its form and properties, and forms a substrate for an integrated virtual reality and telerobotics.
Hall, J. Storrs↗
Simulation of the synergistic low Earth orbit effects of vacuum thermal cycling, vacuum UV radiation, and atomic oxygen
In order to assess the low Earth orbit (LEO) durability of candidate space materials, it is necessary to use ground laboratory facilities which provide LEO environmental effects. A facility combining vacuum thermal cycling and vacuum ultraviolet (VUV) radiation has been designed and constructed at NASA Lewis Research Center for this purpose. This facility can also be operated without the VUV lamps. An additional facility can be used to provide VUV exposure only. By utilizing these facilities, followed by atomic oxygen exposure in an RF plasma asher, the effects of the individual vacuum thermal cycling and VUV environments can be compared to the effect of the combined vacuum thermal cycling/VUV environment on the atomic oxygen durability of materials. The synergistic effects of simulated LEO environmental conditions on materials were evaluated by first exposing materials to vacuum thermal cycling, VUV, and vacuum thermal cycling/VUV environments followed by exposure to atomic oxygen in an RP plasma asher. Candidate space power materials such as atomic oxygen protected polyimides and solar concentrator mirrors were evaluated using these facilities. Characteristics of the Vacuum Thermal Cycling/VUV Exposure Facility which simulates the temperature sequences and solar ultraviolet radiation exposure that would be experienced by a spacecraft surface in LEO are discussed. Results of durability evaluations of some candidate space power materials to the simulated LEO environmental conditions will also be discussed. Such results have indicated that for some materials, atomic oxygen durability is affected by previous exposure to thermal cycling and/or VUV exposure.
Dever, Joyce A.↗
Large-Scale Molecular Dynamics Simulation of the Dehydration of a Suspension of Smectite Clay Nanoparticles
Fine-grained sediments and sedimentary rocks play important roles in a variety of modern energy technologies from petroleum geology to geological carbon sequestration and radioactive waste management. However, despite their utility and ubiquity, many of their properties remain poorly understood. In particular, the ability to predict the permeability and mechanics of these media remains a persistent fundamental challenge in the geosciences. In the present work, we show how large-scale classical molecular dynamics (MD) simulations can help interpret the properties of fine-grained sedimentary material. All-atom simulations containing 30 discrete clay particles are utilized to understand the evolution of a clay nanoparticle suspension during its progressive dehydration. Microstructural (pore size distribution, tortuosity, anisotropy), thermodynamic (enthalpy and free energy of hydration, anion exclusion), mechanical (total suction), and transport properties (diffusion coefficient tensors of water and sodium) are calculated and compared to the experiment. Overall, our results provide new insight into the coupled chemistry, mechanics, and transport properties of disordered nanoparticle assemblages and shed light upon the important role of water films in controlling these properties.
02 PETROLEUM↗
A pseudo-two-dimensional (P2D) model for FeS 2 conversion cathode batteries
Conversion cathode materials are gaining interest for secondary batteries due to their high theoretical energy and power density. However, practical application as a secondary battery material is currently limited by practical issues such as poor cyclability. To better understand these materials, we have, for this study, developed a pseudo-two-dimensional model for conversion cathodes. We apply this model to FeS 2 – a material that undergoes intercalation followed by conversion during discharge. The model is derived from the half-cell Doyle–Fuller–Newman model with additional loss terms added to reflect the converted shell resistance as the reaction progresses. We also account for polydisperse active material particles by incorporating a variable active surface area and effective particle radius. Using the model, we show that the leading loss mechanisms for FeS 2 are associated with solid-state diffusion and electrical transport limitations through the converted shell material. The polydisperse simulations are also compared to a monodisperse system, and we show that polydispersity has very little effect on the intercalation behavior yet leads to capacity loss during the conversion reaction. Finally, we provide the code as an open-source Python Battery Mathematical Modeling (PyBaMM) model that can be used to identify performance limitations for other conversion cathode materials.
25 ENERGY STORAGE↗
Machine Learning for Mapping Multipactor Susceptibility in RF Systems: Capabilities and Generalization Constraints
Multipactor is a surface-driven electron avalanche phenomenon that degrades the performance and reliability of radio-frequency (RF) systems in particle accelerator and vacuum electronics applications. Multipactor behavior in a given device structure is conventionally assessed through susceptibility charts, which provide a parameter-space characterization of the instability. In this work, we assess the capabilities of machine-learning (ML) models to learn and predict such susceptibility charts and analyze the constraints governing their generalization across materials. Using a simulation-derived dataset spanning six distinct secondary-electron-yield material profiles in a canonical two-surface planar geometry, we train supervised regression models and artificial neural networks to predict the time-averaged electron growth rate, δavg, across the relevant parameter space. Model performance is evaluated using metrics that explicitly probe the structure of susceptibility charts, including Intersection over Union, Structural Similarity Index, and correlation analysis. Tree-based ensemble models outperform neural-network models in reconstructing susceptibility regions and in generalizing across material domains. Principal-component analysis reveals disjoint material feature distributions, indicating that the piecewise mode structure of multipactor susceptibility is difficult to represent with a single global model and that generalization is constrained by data coverage rather than by model complexity. An exhaustive reduced-coverage study further shows that sparse material-space coverage can yield mean performance in the same general range but producing large variability in the susceptibility-region overlap. These results clarify the capabilities of ML-based surrogate models for parameter-space characterization of multipactor discharge. They also provide guidance for their appropriate use in RF system design.
43 PARTICLE ACCELERATORS↗
Acousto-ultrasonic characterization of fiber reinforced composites
The acousto-ultrasonic technique combines advantageous aspects of acoustic emission and ultrasonic methodologies. Acousto-ultrasonics operates by introducing a repeating series of ultrasonic pulses into a material. The waves introduced simulate the spontaneous stress waves that would arise if the material were put under stress as in the case of acoustic emission measurements. These benign stress waves are detected by an acoustic emission sensor. The physical arrangement of the ultrasonic (input) transducer and acoustic emission (output) sensor is such that the resultant waveform carries an imprint of morphological factors that govern or contribute to material performance. The output waveform is complex, but it can be quantitized in terms of a 'stress wave factor.' The stress wave factor, which can be defined in a number of ways, is a relative measure of the efficiency of energy dissipation in a material. If flaws or other material anomalies exist in the volume being examined, their combined effect appears in the stress wave factor.
Vary, A.↗
Acousto-ultrasonic characterization of fiber reinforced composites
The acousto-ultrasonic technique combines advantageous aspects of acoustic emission and ultrasonic methodologies. Acousto-ultrasonics operates by introducing a repeating series of ultrasonic pulses into a material. The waves introduced simulate the spontaneous stress waves that would arise if the material were put under stress as in the case of acoustic emission measurements. These benign stress waves are detected by an acoustic emission sensor. The physical arrangement of the ultrasonic (input) transducer and acoustic emission (output) sensor is such that the resultant waveform carries an imprint of morphological factors that govern or contribute to material performance. The output waveform is quite complex, but it can be quantitized in terms of a 'stress wave factor'. The stress wave factor, which can be defined in a number of ways, is essentially a relative measure of the efficiency of energy dissipation in a material. If flaws or other material anomalies exist in the volume being examined, their combined effect will appear in the stress wave factor.
Vary, A.↗
Acousto-ultrasonic characterization of fiber reinforced composites
The advantageous aspects of acoustic emission and ultrasonic methodologies are combined in a technique which operates by introducing a repeating series of ultrasonic pulses into a material. The waves introduced simulate the spontaneous stress waves that would arise if the material were put under stress as in the case of acoustic emission measurements. These benign stress waves are detected by an acoustic emission sensor. The physical arrangement of the ultrasonic (input) transducer and acoustic emission (output) sensor is such that the resultant waveform carries an imprint of morphological factors that govern or contribute to material performance. The output waveform is quite complex, but it can be quantitized in terms of a "stress wave factor". The stress wave factor, which can be defined in a number of ways, is essentially a relative measure of the efficiency of energy dissipation in a material. If flaws or other material anomalies exist in the volume being examined, their combined effect will appear in the stress wave factor.
Vary, A.↗
Intralaminar and Interlaminar Progressive Failure Analysis of Composite Panels with Circular Cutouts
A progressive failure methodology is developed and demonstrated to simulate the initiation and material degradation of a laminated panel due to intralaminar and interlaminar failures. Initiation of intralaminar failure can be by a matrix-cracking mode, a fiber-matrix shear mode, and a fiber failure mode. Subsequent material degradation is modeled using damage parameters for each mode to selectively reduce lamina material properties. The interlaminar failure mechanism such as delamination is simulated by positioning interface elements between adjacent sublaminates. A nonlinear constitutive law is postulated for the interface element that accounts for a multi-axial stress criteria to detect the initiation of delamination, a mixed-mode fracture criteria for delamination progression, and a damage parameter to prevent restoration of a previous cohesive state. The methodology is validated using experimental data available in the literature on the response and failure of quasi-isotropic panels with centrally located circular cutouts loaded into the postbuckling regime. Very good agreement between the progressive failure analyses and the experimental results is achieved if the failure analyses includes the interaction of intralaminar and interlaminar failures.
Goyal, Vinay K.↗
GSAS Tools
SAND2023-06684O GSAS Tools is a web application that manages user access to modeling and simulation tools and promotional material. This software, which is a spiking neural network (SNN) simulator, represents an SNN as a graph of stochastic differential equations and simulates the time-evolution of these equations. It has the capability of reading inputs from file and writing outputs to file, and generally supports experimentation, analysis, and algorithm development using SNNs. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
Noel, Todd↗
Woven ceramic matrix composite surrogate model based on physics-informed recurrent neural network
A recurrent neural network (RNN) based surrogate model is developed to emulate the nonlinear constitutive behavior of woven ceramic matrix composites (CMCs) driven by matrix damage at multiple length scales. Physics-informed constraints are introduced into the surrogate model through regularization to ground the prediction in physics and improve its predictive capabilities. Training data is generated using the multiscale generalized method of cells (MSGMC) approach coupled with a matrix damage model. This coupling permits simulating the nonlinear behavior of woven CMCs based on constituent response at the micro-, meso-, and macroscales. The multiscale repeating unit cell is loaded under non-monotonic conditions including multiple load / unload cycles and tension / compression. The fiber volume fraction as well as the intra- and intertow void volume fractions are also varied in the generation of training data. Therefore, the RNN-based surrogate model is tasked with predicting, as a function of variable input strain sequence and fiber and void volume fractions, the resulting stress versus strain response while satisfying physical constraints such as positive semi-definiteness of the tangent stiffness matrix and linear elastic unloading. Further, the trained surrogate model effectively matches the stress versus strain response and successfully predicts the tangent modulus throughout the loading regime. Neural network based surrogate models can offer efficient alternatives to running computationally intensive multiscale material models to simulate the nonlinear response of large structural models. Therefore the presented work provides evidence towards the feasibility of developing, training, and running such models for CMCs with complex architectures, nonlinear multiaxial material response, and under non-monotonic loading conditions.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Calibration and validation of the foundation for a multiphase strength model for tin
In this work, the Common Model of Multi-phase Strength and Equation of State (CMMP) model was applied to tin. Specifically, calibrations of the strength-specific elements of the CMMP foundation were developed with a combination of experiments and theory, and then the model was validated experimentally. The first element of the foundation is a multi-phase analytic treatment of the melt temperature and the shear modulus for the solid phases. These models were parameterized for each phase based on ab initio calculations using the software VASP (Vienna Ab initio Simulations Package) based on density functional theory. The shear modulus model for the ambient phase was validated with ultrasonic sound speed measurements as a function of pressure and temperature. The second element of the foundation is a viscoplastic strength model for the phase, upon which strength for inaccessible higher-pressure phases can be scaled as necessary. The stress–strain response of tin was measured at strain rates of 10 -3 to 3 x 10 3 s -1 and temperatures ranging from 87 to 373 K. The Preston–Tonks–Wallace (PTW) strength model was fit to that data using Bayesian model calibration. For validation, six forward and two reverse Taylor impact experiments were performed at different velocities to measure large plastic deformation of tin at strain rates up to 10 5 s -1 . The PTW model accurately predicted the deformed shapes of the cylinders, with modest discrepancies attributed to the inability of PTW to capture the effects of twinning and dynamic recrystallization. Some material in the simulations of higher velocity Taylor cylinders reached the melting temperature, thus testing the multiphase model because of the presence of a second phase, the liquid. In simulations using a traditional modeling approach, the abrupt reduction of strength upon melt resulted in poor predictions of the deformed shape and non-physical temperatures. With CMMP, the most deformed material points evolved gradually to a mixed solid–liquid but never a fully liquid state, never fully lost strength, stayed at the melt temperature as the latent heat of fusion was absorbed, and predicted the deformed shape well.
36 MATERIALS SCIENCE↗
Calibration and validation of the foundation for a multiphase strength model for tin
In this work, the Common Model of Multi-phase Strength and Equation of State (CMMP) model was applied to tin. Specifically, calibrations of the strength-specific elements of the CMMP foundation were developed with a combination of experiments and theory, and then the model was validated experimentally. The first element of the foundation is a 10 multi-phase analytic treatment of the melt temperature and the shear modulus for the solid phases. These models were parameterized for each phase based on ab initio calculations using the software VASP (Vienna Ab initio Simulations Package) based on density functional theory (DFT). The shear modulus model for the ambient β phase was validated with ultrasonic sound speed measurements as a function of pressure and temperature. The second element of the foundation is a viscoplastic strength model for the β phase, upon which strength for inaccessible higher-pressure phases can be scaled as necessary. The stress-strain response of tin was measured at strain rates of 10 -3 to 3 x 10 3 s -1 and temperatures ranging from 87 to 373 K. The Preston-Tonks-Wallace (PTW) strength model was fit to that data using Bayesian model calibration. For validation, six forward and two reverse Taylor impact experiments were performed at different velocities to measure large plastic deformation of tin at strain rates up to 10 5 s -1 . The PTW model accurately predicted the deformed shapes of the cylinders, with modest discrepancies attributed to the inability 20 of PTW to capture the effects of twinning and dynamic recrystallization. Some material in the simulations of higher velocity Taylor cylinders reached the melting temperature, thus testing the multiphase model because of the presence of a second phase, the liquid. In simulations using a traditional modeling approach, the abrupt reduction of strength upon melt resulted in poor predictions of the deformed shape and non-physical temperatures. With CMMP, the most deformed material points evolved gradually to a mixed solid-liquid but never fully liquid state, never fully lost strength, 25 stayed at the melt temperature as the latent heat of fusion was absorbed, and predicted the deformed shape well.
36 MATERIALS SCIENCE↗
Artificial intelligence for materials research at extremes
Abstract Materials development is slow and expensive, taking decades from inception to fielding. For materials research at extremes, the situation is even more demanding, as the desired property combinations such as strength and oxidation resistance can have complex interactions. Here, we explore the role of AI and autonomous experimentation (AE) in the process of understanding and developing materials for extreme and coupled environments. AI is important in understanding materials under extremes due to the highly demanding and unique cases these environments represent. Materials are pushed to their limits in ways that, for example, equilibrium phase diagrams cannot describe. Often, multiple physical phenomena compete to determine the material response. Further, validation is often difficult or impossible. AI can help bridge these gaps, providing heuristic but valuable links between materials properties and performance under extreme conditions. We explore the potential advantages of AE along with decision strategies. In particular, we consider the problem of deciding between low-fidelity, inexpensive experiments and high-fidelity, expensive experiments. The cost of experiments is described in terms of the speed and throughput of automated experiments, contrasted with the human resources needed to execute manual experiments. We also consider the cost and benefits of modeling and simulation to further materials understanding, along with characterization of materials under extreme environments in the AE loop. Graphical abstract AI sequential decision-making methods for materials research: Active learning, which focuses on exploration by sampling uncertain regions, Bayesian and bandit optimization as well as reinforcement learning (RL), which trades off exploration of uncertain regions with exploitation of optimum function value. Bayesian and bandit optimization focus on finding the optimal value of the function at each step or cumulatively over the entire steps, respectively, whereas RL considers cumulative value of the labeling function, where the latter can change depending on the state of the system (blue, orange, or green).
36 MATERIALS SCIENCE↗
Simulation of dynamic crystal plasticity with a Lagrangian discontinuous Galerkin hydrodynamic method
Here we present a new Lagrangian modal discontinuous Galerkin (DG) hydrodynamic method that supports a dynamic dislocation based crystal plasticity model for simulating the mechanical behavior of crystallographic materials, both single crystal and polycrystalline, under dynamic conditions. A modal DG approach is used to evolve fields relevant to conservation laws. These fields are approximated by Taylor series polynomials of varying degree. These polynomials describe macro-scale hydrodynamic behavior while their evolution is determined by evaluating the dynamic crystal plasticity model at material points within the element. The dynamic crystal plasticity model is sensitive to the time increment size, with too large of time increments leading to instability in the model. To mitigate this, the temporal evolution of the dynamic crystal plasticity model is achieved with the combination of a sub-incrementing scheme with Heun’s third-order time integration scheme, which is also used to temporally evolve the governing equations. The implementation of the dynamic crystal plasticity model within the DG framework is tested using a 2D approximation of the Taylor impact test with a single crystal material, using quadratic elements that have faces that can bend. In addition to the standard continuous material modeling, we propose a new simulation method that would represent the heterogeneous behavior of polycrystalline microstructures within an element by varying the position and material properties of the material points within the element. This method is demonstrated using random orientation distributions on materials points that are arranged in both structured and random configurations.
42 ENGINEERING↗
Icarus Application to Dragonfly Heatshield
Icarus, an in-house material response solver developed at NASA Ames, is applied to perform 2-D bondline-temperature analysis on the heatshield surface of the latest Dragonfly entry system design for a mission to Titan. Two axisymmetric domains are used: the near-shoulder region and the entire heatshield. The heatshield consists of a PICA TPS bonded to a layered system of Aluminum honeycomb wrapped by an M55J carbon-fiber facesheet. The backshell, which is also included for more accurate analysis, consists of similar materials layered. Icarus simulations using both domains indicate that the in-plane thermal conductivity property of the M55J facesheet is observed to play a dominant role in bondline temperature. Similarly, the maximum bondline temperature is found around the PICA-tile-interface region instead of the near-shoulder region or the stagnation point based on the current trajectory using the orthotropic properties of M55J. Therefore, these findings indicate the significance of 2-D or higher-dimensional material modeling analysis in fully understanding how bondline temperature behaves across the heatshield and obtaining a basis for TPS design locations. Verification at different radial locations ahead of the shoulder, where thermal conduction is close to 1-D, between Icarus (2-D), Icarus (1-D), and FIAT is performed and good agreement is observed. With the entire heatshield domain, the total mass loss of the aeroshell materials is approximately 3% of their initial mass potentially due to thermal decomposition of phenolic resins inside PICA under the current high-heating environment.
TPS material modeling↗