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At least 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Verification of Upcoming MCNP Features For Estimating Nuclear Data Sensitivities in Fixed Source Simulations [Abstract]

Predictive simulation codes, like the Monte Carlo N-Particle (MCNP) transport code, are used throughout the nuclear community. These simulations are based on nuclear data. Maximizing the accuracy and precision of nuclear data maximizes the accuracy and precision of the overall simulation. This is imperative to applications that rely on simulations. For example, improving nuclear data for special nuclear material improves simulation accuracy in stockpile stewardship applications, which results in larger safety margins and decreased operational costs. The improvement and validation of nuclear data is completed through integral benchmark experiments. Past benchmarks have primarily been limited to focus on the effective multiplication factor ($\kappa$ eff ); broadening the purview of benchmarks beyond $\kappa$ eff -dependent nuclear data addresses nuclear data deficiencies. Different response types depend on different areas of nuclear data. This dependence is quantified as nuclear data sensitivity: the change in response due to perturbation of a contributing parameter. The larger the nuclear data sensitivity of a response, the more the experiment is influenced by the uncertainties of the nuclear data. The optimization of nuclear data sensitivities in future benchmarks would result in more detailed validation of lesser studied areas of nuclear data. Currently, direct sensitivity capabilities are not easily found for all experiment types and parameters. An MCNP tool to directly estimate the cross section sensitivities of tallied values is under development. Additionally, updates have been made to the perturbation feature of MCNP, which can be used in a less direct approach to estimating sensitivities. This work verifies these features to estimate nuclear data sensitivities in fixed source simulations of a 4.5-kg sphere of alpha- phase weapons-grade plutonium surrounded by differing amounts of copper and polyethylene. Integrated estimates made using MCNP’s tools were found to statistically agree with integrated estimates made from manual perturbation of nuclear data proving the validity of the MCNP tools.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1991

Simulation studies for surfaces and materials strength

Investigations were carried out in the following three areas. First, a simulation study was conducted to investigate the early stages of a nucleation and growth mechanism taking place on diamond surfaces. The energetically most favorable binding sites were calculated along with the binding energies for up to three carbon atoms deposited on the planes of a diamond crystal. Secondly, calculations were carried out to analyze energies and structural properties for restructured patterns of the surface of a diamond. Two different model functions, developed recently for carbon, were employed in the calculations. Lastly, a crack propagation process for the graphitic basal plane, was investigated using a molecular dynamics technique. Interactions among carbon atoms in the system were calculated using the Tersoff potential energy function.

Halicioglu, Timur↗
NASA NTRSDate not supplied

Simulating Ordinary Chondrite (Tamdakht) Ablation at the Hypersonic Materials Environmental Test System (HyMETS) Facility.

NASA’s Science Mission Directorate (SMD) created the Asteroid Threat Assessment Project (ATAP) to inform decision-makers of risks associated with Potentially Hazardous Objects (PHOs), which may pose an existential threat to human civilization. ATAP assesses risk, in part, by developing analytical physics-based damage models which draw upon data collected during material property characterization efforts, entry simulations, hazard simulations, and ground-based arc-jet testing. Therefore, a pathfinder test campaign was con-ducted at the Hypersonic Materials Environmental Test System (HyMETS) at the NASA Langley Re-search Center to investigate the ablation mechanisms of an ordinary chondrite (Tamdakht H5) and a terrestrial analog (basalt). The HyMETS facility is a 400 kW constricted arc heater used to screen material performance under simulated aerothermal conditions of hypersonic flow.1 Facility conditions were chosen to simulate Earth entry conditions be-tween the upper-mesosphere and the lower thermo-sphere. The ablation mechanisms of Tamdakht and bas-alt are illustrated with still images collected from high-speed video cameras (fig. 1, Tamdakht). Tamdakht produces a relatively stable melt flow, as evidenced by the perpetual growth of a flange at the sidewall and the exiguous detachment of molten material into the flow. Furthermore, an inspection of high-speed video and chemical analysis of the post-test melt layer suggests that the dominant mode of mass loss for Tamdakht, at these test conditions, is expressed through the vaporization of volatiles (e.g., iron, potassium, sodium, phosphorus, and sulfur). The vaporization rates of Tamdakht are greater than basalt under the most extreme test conditions, which leads to an enhanced blowing layer, in-creased thermal shielding, and reduced recession. The ablation mechanisms of basalt are markedly different from Tamdakht and are attributed to the presence of hydrated minerals dispersed at irregular intervals throughout the silicate matrix. Rapid de-composition of secondary minerals and the subsequent formation of water lead to the ejection of the subsurface and overlying melt layer. Furthermore, the presence of water is suspected of lowering the viscosity of the melt layer resulting in increased mass loss near the edge of the test article where drag forces overcome rheological properties (fig. 1, bas-alt). Finally, the results will be discussed in the broader context of meteorite-based material response model development and the potential impact on the Asteroid Threat Assessment Project (ATAP).

Brody K. Bessire↗
DOE OSTI2024

MatCal Users Guide: Release 1.3.0

Any continuum mechanics model will require three components: (1) a discretized geometry of the boundary value problem being studied, (2) the partial differential equations to be solved, and (3) the initial conditions and boundary conditions for the problem. To describe material behavior in these computational models, material models contribute to (2) the underlying equations and, occasionally, to (3) the initial conditions for the simulation. These material models can exhibit a mathematical form that is empirically based, based on first principles, or developed from both empirical observations and known physics. In general, these models are meant to represent a class of materials with well understood behavior. As a result, material models have parameters that must be tuned or calibrated so that the model response matches characterization data available for the specific material it is intended to represent when used to simulate a specific system. For simple models, such as isotropic, linear elastic materials in solid mechanics, this calibration process can be a simple analytical calculation directly extracting the parameters from experimental measurements. For complex models that have many inputs and require many characterization datasets to adequately identify the material behavior, the model calibration process can require an inverse problem approach where an optimization is performed to tune the model parameters to the available data.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Canister Centerline Cooling Experiments for DPF5-336 Reference Material Made with ERV3b Salt Simulant

This report provides experimental details and results on the elemental distributions (compositions), crystalline/amorphous phase distribution, and microstructures for DPF5-336 reference materials made with ERV3b salt that were subjected to the canister centerline cooling (CCC) heat treatment process. To fabricate these materials, ammonium dihydrogen phosphate was mixed with ERV3b salt simulant and a dechlorination procedure was run in the generation-2 dechlorinator at the Pacific Northwest National Laboratory up to 600°C in an alumina crucible. After the dechlorination process, the product was removed from the crucible, lightly crushed, loaded into a new alumina crucible, Fe 2 O 3 was added, and this was vitrified at 1100°C for 1 hour and quenched. The quenched material was mostly amorphous but showed some small diffraction peaks attributed to Li 3 Fe 2 (PO 4 ) 3 . This quenched material was ground to a fine particle size in a milling chamber, and this was subjected to the CCC cooling profile in duplicate within alumina and fused quartz crucibles. The CCC experiment is designed to simulate the slowest cooling profile (the vertical centerline) of a 0.61-m diameter canister where the slower cooling profile often results in the production of a variety of different crystalline phases upon formation of which result in a residual glass of unknown composition and properties. The information gleaned from these types of experiments will lead to improved composition-property predictions for materials with similar compositions. The results of the alumina and fused quartz experiments show different microstructures but similar diffraction patterns providing evidence of similar crystalline phases being present in each sample. Both samples had monazite [i.e., (Ce,Nd)PO 4 ] and mixtures of phases containing P, Fe, and alkali metal elements. This report completes the milestone M4FT-23PN030104042 with details provided in Appendix B.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Metal–ceramic composite structures for fabrication of high power density plasmonic devices

The recent decade brought many advances to plasmonics, but high power density plasmonic antennas designed to behave as heaters or operate in high temperature environments are still facing material stability challenges preventing their ultimate use. Gold has been the optimal choice among plasmonic materials but experiences morphology changes at temperature that result in device efficiency reduction and failure. Bulk titanium nitride has been explored as a solution but has deal-breaking tradeoffs in device quality factor. In this paper, we explore via proof-of-concept the use of a metal–ceramic composite structure to determine whether a bulk Au nanorod can provide strong plasmonic resonances while coated with an ultrathin conformal layer of titanium nitride or silica to provide morphological stability and sufficient plasmonic activity without excessive resonance quality degradation. We show SEM-level morphological stability for temperatures up to 500 °C with coatings below 4 nm. Computer modeling suggests the ultrathin titanium nitride has detrimental effects on the strong plasmonic resonances of a Au nanorod. Here, we then looked into other possible coatings for solutions to stabilize high power density plasmonic antennas including plasmonic oxides, metal adhesion layers, and silica, the latter appearing to be the best option while lowering the overall peak electric field intensity, the silica increases the electric field intensity at its boundary.

36 MATERIALS SCIENCE↗
NASA NTRS2010

Evolving Material Porosity on an Additive Manufacturing Simulation with the Generalized Method of Cells

The effect of material porosity on final part distortion and residual stresses in a selective laser sintering manufacturing simulation is presented here. A time-dependent thermomechanical model is used with the open-source FEA software CalculiX. Effective homogenized material properties for Inconel 625 are precomputed using NASA’s Micromechanics Analysis Code with Generalized Method of Cells (MAC/GMC). The evolving porosity of the material is estimated with each pass of the laser beam during simulation runtime. A comparison with a homogenous model and the evolving model shows that the evolving porous model predicts larger distortions with greater residual stresses.

Silva, Luis F.↗
NASA NTRS2024

Evaluation of the Mechanical Properties of Innovative Spacecraft Materials Under a Low Earth Orbit-Simulated Atomic Oxygen Environment

External spacecraft materials are crucial for protecting satellites from the harsh space environment. This study evaluates the radiation effects of low Earth orbit (LEO) space weather, focusing on high-energy electron and atomic oxygen (AO) exposure, on several modern spacecraft materials. The materials under investigation are “flight duplicates" of samples scheduled for launch as part of the Materials International Space Station Experiment Flight Facility (MISSE-FF) mission in 2025. To observe changes in stress distribution within weathered polymers, the experimental setup based on the photo elasticity technique was developed.

Yuliya Kuznetsova↗
DOE OSTI2022

Sampling lattices in semi-grand canonical ensemble with autoregressive machine learning

Calculating thermodynamic potentials and observables efficiently and accurately is key for the application of statistical mechanics simulations to materials science. However, naive Monte Carlo approaches, on which such calculations are often dependent, struggle to scale to complex materials in many state-of-the-art disciplines such as the design of high entropy alloys or multi-component catalysts. To address this issue, we adapt sampling tools built upon machine learning-based generative modeling to the materials space by transforming them into the semi-grand canonical ensemble. Furthermore, we show that the resulting models are transferable across wide ranges of thermodynamic conditions and can be implemented with any internal energy model U, allowing integration into many existing materials workflows. We demonstrate the applicability of this approach to the simulation of benchmark systems (AgPd, CuAu) that exhibit diverse thermodynamic behavior in their phase diagrams. Finally, we discuss remaining challenges in model development and promising research directions for future improvements.

36 MATERIALS SCIENCE↗
NASA NTRS2018

Recent Developments to the Porous Microstructure Analysis (PuMA) Software

The Porous Microstructure Analysis (PuMA) software is a suite of tools for the analysis of porous materials and generation of material microstructures. From microstructural data, often obtained through X-ray microtomography, PuMA can determine a number of effective material properties and perform material response simulations. Version 2.2 includes capabilities for computing volume fractions, porosity, specific surface area, effective thermal and electrical conductivities, and continuum and rarefied diffusive tortuosity. PuMA can also simulate competitive diffusion/reaction processes at the micro-scale, such as surface oxidation. In this poster, recent advancements to the PuMA software are detailed, including the full refactoring of PuMA into v3.0, a new module to compute heat conduction in anisotropic materials, a particle method for simulating molecular beam experiments, a new finite-volume Laplace solver, complex fibrous material generation, woven material generation, and a coupling of PuMA with the DAKOTA software for advanced statistics.

PuMA↗
NASA NTRSDate not supplied

ICME for NASA Aerospace Applications: Batteries for Electric Aviation

NASA’s approach to computational materials modeling is detailed in the NASA Vision 2040 Roadmap for Multiscale Modeling and Simulation of Materials and Systems. This report is in the spirit of national initiatives such as the Material Genome Initiative (MGI), Integrated Computational Materials Engineering (ICME), and others. We utilize a combination of fundamental modeling, computational high-throughput screening, and data science methods, e.g., machine learning, are used to find innovative solutions to NASA or national technology challenges. Applications of interest are wide ranging from advanced alloys to batteries to coatings, among others. In this talk, we present three examples for recent work related to NASA applications. First, doping advanced sulfur battery cathodes with selenium boosts electrical conductivity important for electric aircraft applications. First principles calculations will be discussed that result in compositional design maps for these materials. Second, development of icephobic coatings is important to mitigate safety hazards associated with icing for aircraft. Molecular dynamics simulations are reported for ice-surface interfaces to understand adhesion mechanisms and help screen optimal ice-phobic coatings. Third, shape memory alloys have numerous applications as actuators, superelastic materials, etc. for aerospace. We report machine learning models that predict martensitic transition temperatures across a broad swath of compositional space.

John Lawson↗
NASA NTRSDate not supplied

MEDLI2: Ablator Models for Flight

During the entry of Mars Science Laboratory (MSL) the heatshield was equipped with the instrumentation suite Mars Entry, Descent, and Landing Instruments (MEDLI). In-depth thermocouple (TC) data was used to reconstruct the surface heating[1,2]. Discrepancies between MEDLI’s flight heating data and predicted thermal response and recession[3] led to a campaign to characterize and test Mars2020 and MEDLI2 (Mars Entry, Descent, and Landing Instrument 2)[4] flight-lot thermal protection materials (TPS) at relevant ground test conditions. This paper covers the development and validation of flight-lot material response models for the heatshield material Phenolic Impregnated Carbon Ablator (PICA) and the backshell material SLA-561V (Super Lightweight Ablator). Virgin and char properties such as thermal conductivity and emissivity were updated as a function of temperature and pressure in the MEDLI2 specific material response models. Other material properties were characterized and determined to be similar to prior TPS material measurements used for the heritage models. MEDLI2 also investigated the impact of the protective coating that was applied to MSL and Mars2020’s PICA heatshield to reduce the particulate matter shedding. Evidence from arc jet experiments on coated PICA showed that although there were no adverse effects on the performance of the TPS, there were differences in the in-depth temperatures and reduced recession compared to material response predictions of uncoated PICA.[5,6]. Fully Implicit Ablation and Thermal response (FIAT)[7] material response simulations were conducted with the heritage and MEDLI2 flight-lot material models. FIAT-predicted in-depth temperature responses and recession predictions were compared to flight-lot certification ground-test arc jet test data and MEDLI2 thermocouple data.

Materials Response↗
NASA NTRS2008

A Modeling Technique and Representation of Failure in the Analysis of Triaxial Braided Carbon Fiber Composites

Quasi-static tests have been performed on triaxially braided carbon fiber composite materials with large unit cell sizes. The effects of different fibers and matrix materials on the failure mode were investigated. Simulations of the tests have been performed using the transient dynamic finite element code, LS-DYNA. However, the wide range of failure modes observed for the triaxial braided carbon fiber composites during tests could not be simulated using composite material models currently available within LS-DYNA. A macroscopic approach has been developed that provides better simulation of the material response in these materials. This approach uses full-field optical measurement techniques to measure local failures during quasi-static testing. Information from these experiments is then used along with the current material models available in LS-DYNA to simulate the influence of the braided architecture on the failure process. This method uses two-dimensional shell elements with integration points through the thickness of the elements to represent the different layers of braid along with a new analytical method for the import of material stiffness and failure data directly. The present method is being used to examine the effect of material properties on the failure process. The experimental approaches used to obtain the required data will be described, and preliminary results of the numerical analysis will be presented.

Littell, Justin D.↗
NASA NTRS2008

A Modeling Technique and Representation of Failure in the Analysis of Triaxial Braided Carbon Fiber Composites

Quasi-static tests have been performed on triaxially braided carbon fiber composite materials with large unit cell sizes. The effects of different fibers and matrix materials on the failure mode were investigated. Simulations of the tests have been performed using the transient dynamic finite element code, LS-DYNA. However, the wide range of failure modes observed for the triaxial braided carbon fiber composites during tests could not be simulated using composite material models currently available within LS-DYNA. A macroscopic approach has been developed that provides better simulation of the material response in these materials. This approach uses full-field optical measurement techniques to measure local failures during quasi-static testing. Information from these experiments is then used along with the current material models available in LS-DYNA to simulate the influence of the braided architecture on the failure process. This method uses two-dimensional shell elements with integration points through the thickness of the elements to represent the different layers of braid along with a new analytical method for the import of material stiffness and failure data directly. The present method is being used to examine the effect of material properties on the failure process. The experimental approaches used to obtain the required data will be described, and preliminary results of the numerical analysis will be presented.

Littell, Justin D.↗
DOE OSTI2021

VERA-Grizzly Ex-Core Calculations: Watts Bar Unit 1 Cycles 1-2

The critical structures that comprise light-water reactor (LWR) nuclear power plants are subjected to operating environments that can challenge their integrity. Structures in close proximity to the reactor core, such as the reactor pressure vessel (RPV) and the biological shield wall, are subjected to high levels of radiation emanating from the core, as well as elevated temperatures. As the US fleet of operating LWRs ages, the effects of these operating environments on the integrity of these structures must be considered to ensure their continued safe operation. Extending the lifetime of commercial reactors and maintaining the aging reactor fleet require accurate prediction of the exposure of ex-core components to neutron and photon radiation. In particular, concrete degradation studies must be performed to evaluate the safety and long-term operation of reactors with lifetime extensions. The concrete reactor bioshield is important for providing radiological protection during operation and must last for the entire lifetime of the reactor. Recent interest in lifetime extensions furthers the need to accurately simulate concrete material degradation in the reactor bioshield. As a result of this need, the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program has funded this study to couple its tools, Virtual Environment for Reactor Applications (VERA) and Grizzly. VERA allows users to set up models to calculate time-dependent and fully coupled solutions (with thermal feedback) for ex-core quantities of interest such as vessel and coupon fluence and detector responses for multiple statepoints and cycles. Grizzly is a finite-element application based on the Multiphysics Object Oriented Simulation Environment (MOOSE) framework that is used to enable aging materials calculations. This report highlights the work performed to calculate the fluence in the vessel and concrete for Watts Bar Nuclear Plant Unit 1 (WBN1) Cycles 1 and 2. The fluences obtained from VERA were successfully transferred to Grizzly using a Python script. Four simulations were run with Grizzly: (1) the Mazars model with the initial Young’s modulus being the instantaneous modulus, (2) the Mazars model with the initial Young’s modulus being the delayed modulus, (3) the Mazars model with the initial Young’s modulus being the delayed modulus with the addition of the effects of micro-damage caused by irradiation, and (4) the Mazars model with the initial Young’s modulus being the instantaneous modulus, and with the addition of micro-damage and creep. Details regarding the methods used to obtain the fluence and the statistical errors associated with the VERA Monte Carlo Shift calculations are discussed in greater detail in this report. The results obtained from the four Grizzly models are also presented in this report.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2025

A generalized and adaptable tensor-contraction-based cluster expansion formalism for multicomponent solids

Density functional theory (DFT)-based simulations of materials have first-principles accuracy, but are very computationally expensive. For simulating various properties of multi-component alloys, the cluster expansion (CE) technique has served as the standard workaround to improve computational efficiency. However, the standard CE technique is difficult to extend to exotic and/or low-symmetry lattices, often implemented via iteration over particular cluster types, which must be enumerated per lattice structure. In this work, we introduce the tensor cluster expansion (TCE), implemented in the open-source code tce-lib, which maps correlation functions to mixed tensor contractions, eliminating the need to iterate over cluster types and additionally making the calculation of correlation functions well-suited for massively parallel architectures like GPUs. We show that local interaction energies are an immediate consequence of the TCE formalism, yielding nearly $\mathcal{O}$(1) energy difference calculations. We then use this formalism to fit CE models for the TaW and CoNiCrFeMn systems, and use these models to respectively compute the enthalpy of mixing curve and Cowley short-range order parameters, showing excellent agreement with ground truth data.

Cluster expansion↗
DOE OSTI2022

Extensive Attention Mechanisms in Graph Neural Networks for Materials Discovery

We present our research where attention mechanism is extensively applied to various aspects of graph neural net- works for predicting materials properties. As a result, surrogate models can not only replace costly simulations for materials screening but also formulate hypotheses and insights to guide further design exploration. We predict formation energy of the Materials Project and gas adsorption of crystalline adsorbents, and demonstrate the superior performance of our graph neural networks. Moreover, attention reveals important substructures that the machine learning models deem important for a material to achieve desired target properties. Our model is based solely on standard structural input files containing atomistic descriptions of the adsorbent material candidates. We construct novel methodological extensions to match the prediction accuracy of state-of-the-art models some of which were built with hundreds of features at much higher computational cost. We show that sophisticated neural networks can obviate the need for elaborate feature engineering. Our approach can be more broadly applied to optimize gas capture processes at industrial scale.

Cong, Guojing↗