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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

A Quantum Analog Coprocessor for Correlated Electron Systems Simulation

Analog quantum simulation is an approach for studying physical systems that might otherwise be computationally intractable to simulate on classical high-performance computing (HPC) systems. The key idea behind analog quantum simulation is the realization of a physical system with a low-energy effective Hamiltonian that is the same as the low-energy effective Hamiltonian of some target system to be studied. Purpose-built nanoelectronic devices are a natural candidate for implementing the analog quantum simulation of strongly correlated materials that are otherwise challenging to study using classical HPC systems. However, realizing devices that are sufficiently large to study the properties of a non-trivial material system (e.g., those described by a Fermi-Hubbard model) will eventually require the fabrication, control, and measurement of at least 0(10) quantum dots, or other engineered quantum impurities. As a step toward large-scale analog or digital quantum simulation platforms based on nanoelectronic devices, we propose a new approach to analog quantum simulation that makes use of the large Hilbert space dimension of the electronic baths that are used to adjust the occupancy of one or a few engineered quantum impurities. This approach to analog quantum simulation allows us to study a wide array of quantum impurity models. We can further augment the computational power of such an approach by combining it with a classical computer to facilitate dynamical mean-field theory (DMFT) calculations. DMFT replaces the solution of a lattice impurity problem with the solution of a family of localized impurity problems with bath couplings that are adjusted to satisfy a self-consistency condition between the two models. In DMFT, the computationally challenging task is the high-accuracy solution of an instance of a quantum impurity model that is determined self-consistently in coordination with a mean-field calculation. We propose using one or a few engineered quantum impurities with adjustable couplings to baths to realize an analog quantum coprocessor that effects the solution of such a model through measurements of a physical quantum impurity, operating in coordination with a classical computer to achieve a self-consistent solution to a DMFT calculation. We focus on implementation details relevant to a number of technologies for which Sandia has design, fabrication, and measurement expertise. The primary technical advances outlined in this report concern the development of a supporting modeling capability. As with all analog quantum simulation platforms, the successful design and operation of individual devices depends critically on one's ability to predict the effective low-energy Hamiltonian governing its dynamics Our project has made this possible and lays the foundation for future experimental implementations.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1990

Aeolian Removal of Dust Types from Photovoltaic Surfaces on Mars

Dust elevated in local or global dust storms on the Martian surface could settle on photovoltaic (PV) surfaces and seriously hamper their performance. Using a recently developed technique to apply a uniform dust layer, PV surface materials were subjected to simulated Martian winds in an attempt to determine whether natural aeolian processes on Mars would sweep off the settled dust. Three different types of dust were used. The effects of wind velocity, angle of attack, height above the Martian surface, and surface coating material were investigated. It was found that arrays mounted on an angle of attack approaching 45 deg show the most efficient clearing. Although the angular dependence is not sharp, horizontally mounted arrays required much higher wind velocities to clear off the dust. From this test it appears that the arrays may be erected quite near the ground, but previous studies have suggested that saltation effects can be expected to cause such arrays to be covered by soil if they are set up less than about a meter from the ground. Particle size effect appear to dominate over surface chemistry in these experiments, but additional tests are required to confirm this.

Gaier, James R.↗
DOE OSTI2020

Summary of Structural Material Modeling Development for the NEAMS Program in Fiscal Year 2020

This report summarizes work performed during Fiscal Year (FY) 2020 at Idaho National Laboratory (INL) for the U.S. Department of Energy?s Nuclear Engineering Advanced Modeling and Simulation (NEAMS) program for the Structural Materials and Chemistry Technical Area in the work package entitled "MS- 20IN050104 - Structural Materials - INL." The Structural Materials and Chemistry Technical Area is a relatively new component of the NEAMS program, and is currently focusing on developing simulation capabilities to support the deployment of nuclear energy in the areas of molten salt reactor chemistry, light water reactor (LWR) structural material degradation, and structural material behavior for advanced reactor applications. INL performed work for to advance capabilities for simulation of structural material behavior in both LWR and advanced reactor applications in the work described here.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

First-Principles Investigation of Electrides Derived from Sodalites

Recently, electride materials, with excess anionic electrons confined in their empty space, have received growing attention due to their promising applications in catalysis, nonlinear optics, and spin electronics. However, the utilization of electride materials is limited by their thermal instability. Here, we introduce an alternative way to achieve the localized anionic electron states via the removal of highly symmetric Wyckoff sites of anions from the existing sodalite compounds. Using four halide sodalites as the parental structures, our simulation reveals that the materials after the removal of anionic halide sites exhibit typical electride behaviors that are characterized by the existence of localized electronic states near the Fermi level. Compared to most previously studied electrides, these materials are expected to be more thermally stable due to the complex structural framework and thus promising for practical applications. Among them, Na 4 (AlSiO 4 ) 3 manifests magnetic electronic structure. We demonstrate that this magnetism originates from a highly localized excess electron state surrounded by electropositive alkaline cations. Here, our results suggest Na 4 (AlSiO 4 ) 3 could be a promising spintronics component, thus encouraging further experimental study.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A Combined Computational and Mathematical Analysis of Interconnect Fatigue Potential in Photovoltaic Modules

A finite element model of a 60-cell monocrystalline silicon glass-polymer photovoltaic module was simulated with ±1.0 kPa and ±2.4 kPa loads applied to the glass to calculate the deformation under load. Cell-to-cell displacements were used to approximate interconnect strain and stress. A mathematical fatigue cycle life relation was fitted to data for the interconnect material (copper), to generate a life prediction at each interconnect location based on the local stress means, reversal extents, and amplitudes. Interconnect stress was found to be significantly asymmetric about zero despite symmetric positive and negative module loads due to laminate thickness offsets about the neutral plane and the effects of module framing. Cycle life results indicated that interconnect fatigue failure was unlikely to occur over a 30-year lifetime of conservative wind and snow load cycles since the typical cell design feature of leaving some unconstrained length between the cell edge and first solder pad increases the effective gauge length and decreases the stress levels below the material endurance limit. Follow-up analyses found that 3.6 mm and 6.4 mm were the minimum unconstrained lengths required to survive the assumed lifetime of wind and snow cycles, respectively, confirming that typical industrial module constructions with 8–15 mm unconstrained lengths should survive conservatively. Notably, large magnitude, low-cycle snow loading was consistently the limiting factor requiring a longer unconstrained interconnect length. Finally, insights and workflows from this study inform module interconnection design limits for survival against mechanical fatigue in deployment environments.

14 SOLAR ENERGY↗
NASA NTRS2015

On The Development of Additive Construction Technologies for Application to Development of Lunar/Martian Surface Structures Using In-Situ Materials

For long-duration missions on other planetary bodies, the use of in-situ materials will become increasingly critical. As man's presence on these bodies expands, so must the breadth of the structures required to accommodate them including habitats, laboratories, berms, radiation shielding for natural radiation and surface reactors, garages, solar storm shelters, greenhouses, etc. Planetary surface structure manufacturing and assembly technologies that incorporate in-situ resources provide options for autonomous, affordable, pre-positioned environments with radiation shielding features and protection from micrometeorites, exhaust plume debris, and other hazards. This is important because gamma and particle radiation constitute a serious but reducible threat to long-term survival of human beings, electronics, and other materials in space environments. Also, it is anticipated that surface structures will constitute the primary mass element of lunar or Martian launch requirements. The ability to use in-situ materials to construct these structures will provide a benefit in the reduction of up-mass that would otherwise make long-term Moon or Mars structures cost prohibitive. The ability to fabricate structures in situ brings with it the ability to repair these structures, which allows for self-sufficiency necessary for long-duration habitation. Previously, under the auspices of the MSFC In Situ Fabrication and Repair (ISFR) project and more recently, under the joint MSFC/KSC Additive Construction with Mobile Emplacement (ACME) project, the MSFC Surface Structures Group has been developing materials and construction technologies to support future planetary habitats with in situ resources. One such technology, known as Contour Crafting (additive construction), is shown in Figure 1, along with a typical structure fabricated using this technology. This paper will present the results to date of these efforts, including development of novel nozzle concepts for advanced layer deposition using the Contour Crafting process. This process, conceived initially for rapid development of cementitious structures on Earth, also lends itself exceptionally well to the automated fabrication of planetary surface structures using minimally processed regolith as aggregate, and imported binder material or binders developed from in situ materials. This process has been used successfully in the fabrication of construction elements using lunar regolith simulant and Mars regolith simulant, both with various binder materials. These binder materials have resulted from extensive evaluation and include both "imported" binder materials that might be launched from Earth as well as some binder materials that can theoretically also be derived from existing regolith materials. They were chosen to 1) reduce penetrating radiation as much as possible, primarily with hydrogen-bearing polymers, 2) attempt to provide an air-tight structure, 3) sufficiently mix and adsorb to regolith grains for strength, 4) maximize tolerance to day-night thermal cycling, 5) possibly increase electrical conductivity to dissipate any accumulated static charge, and 6) ease their application on planetary surfaces (specifically, the accommodation of reduced atmosphere and lack of heat sinks). Some of these materials have been tested with respect to radiation mitigation, micrometeorite resistance, and resistance to larger, slower-traveling pieces of regolith impinging on the surface, simulating nearby launch and landing activities. Conceptual designs for a Continuous Feedstock Delivery/Mixing System (CFDMS) will also be presented and future planned activities will be discussed as well.

Werkheiser, Niki↗
NASA NTRS2014

Effects of Laser Wavelength on Ablator Testing

Wavelength-dependent or spectral radiation effects are potentially significant for thermal protection materials. NASA atmospheric entry simulations include trajectories with significant levels of shock layer radiation which is concentrated in narrow spectral lines. Tests using two different high powered lasers, the 10.6 micron LHMEL I CO2 laser and the near-infrared 1.07 micron fiber laser, on low density ablative thermal protection materials offer a unique opportunity to evaluate spectral effects. Test results indicated that the laser wavelength can impact the thermal response of an ablative material, in terms of bond-line temperatures, penetration times, mass losses, and char layer thicknesses.

thermal protection↗
DOE OSTI2026

The high explosives & affected targets (HEAT) dataset

Artificial Intelligence (AI) surrogate models offer a computationally efficient alternative to full-physics simulations, yet no existing datasets are publicly available for training, testing, and validation of machine learning models of the dynamics of high-explosive driven shocks through multiple materials. Shock propagation through materials is a computationally challenging problem because simulations must include material-specific equations of state (EOS) along with descriptions of other physical processes such as plastic deformation, phase change, damage processes, fluid instabilities, and multi-material interactions. Shocks are typically initiated by high-velocity impacts or explosive loading. The latter case necessitates the addition of models of reactive materials to represent high-explosive (HE) detonation. Here, to address the lack of an expansive dataset for multi-material shock propagation in the AI/ML community, we present the High-Explosives and Affected Targets (HEAT) Dataset. HEAT is a physics-rich collection of two-dimensional, cylindrically symmetric, simulations generated using an Eulerian, multi-material, shock-propagation code developed at Los Alamos National Laboratory. The dataset includes two partitions: (1) the expanding shock-cylinder (CYL) simulations, Figs. 1, and (2) the Perturbed Layered Interface (PLI) simulations, Fig. 2. Entries in both partitions consist of time series of arrays of thermodynamic fields (pressure, density, and temperature), kinematic fields (position and velocity), and additional fields that depend on thermodynamic and/or kinematic fields (e.g., material stress). Materials in the CYL partition include solids (aluminium, copper, depleted uranium, stainless steel, tantalum, and a generic polymer), a liquid (water), gases (air, nitrogen), and a generic detonating material (high explosive, HE). The PLI partition spans a highly varying geometry but consists of fixed materials across entries: Copper, aluminium, stainless steel, generic polymer, and generic HE. HEAT captures critical phenomena such as momentum transfer, shock propagation, plastic deformation, and thermal effects, making HEAT a valuable benchmark for development of AI/ML emulation of multi-material shock propagation.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Accelerating high-strain continuum-scale brittle fracture simulations with machine learning

Failure in brittle materials under dynamic loading conditions is a result of the propagation and coalescence of microcracks. Simulating this discrete crack evolution at the continuum level is computationally expensive or, in some cases, intractable, resulting in the need to make broad assumptions or neglect key physics. In this work, we have developed an approach using machine learning that overcomes the current inability to represent meso-scale physics at the macro-scale. Our approach leverages damage and stress data from a computationally expensive high-fidelity model that explicitly resolves microcrack behavior to build an inexpensive machine learning emulator. Once trained, the machine learning emulator is used to predict the evolution of crack length statistics, which then informs a continuum-scale constitutive model. This results in a significant speed-up of the workflow by four orders of magnitude. Both the machine learning emulator and the continuum-scale model are validated against the high-fidelity model and experimental data, respectively, showing excellent agreement. There are two key findings. The first is that we can reduce the dimensionality of the problem, establishing that the machine learning emulator only needs the length of the longest crack and one of the maximum stress components to capture the necessary physics. Another compelling finding is that the emulator can be trained in one experimental setting and transferred successfully to predict behavior in a different setting.

36 MATERIALS SCIENCE↗
NASA NTRS1992

Materials technology for Stirling space power converters

This program was conducted in support of the NASA LeRC development of the Stirling power converter (SPC) for space power applications. The objectives of this contract were: (1) to perform a technology review and analyses to support the evaluation of materials issues for the SPC; (2) to evaluate liquid metal compatibility issues of the SPC; (3) to evaluate and define a transient liquid phase diffusion bonding (TLPDB) process for the SPC joints to the Udimet 720 heater head; and (4) to evaluate alternative (to the TLPDB) joining techniques. In the technology review, several aspects of the current Stirling design were examined including the power converter assembly process, materials joining, gas bearings, and heat exchangers. The supporting analyses included GLIMPS power converter simulation in support of the materials studies, and system level analysis in support of the technology review. The liquid metal compatibility study evaluated process parameters for use in the Stirling power converter. The alternative joining techniques study looked at the applicability of various joining techniques to the Stirling power converter requirements.

Baggenstoss, William↗
NASA NTRS1999

Real-Time Investigation of Solidification of Metal Matrix Composites

Casting of metal matrix composites can develop imperfections either as non- uniform distributions of the reinforcement phases or as outright defects such as porosity. The solidification process itself initiates these problems. To identify or rectify the problems, one must be able to detect and to study how they form. Until, recently this was only possible by experiments that employed transparent metal model organic materials with glass beads to simulate the reinforcing phases. Recent results obtained from a Space Shuttle experiment (using transparent materials) will be used to illustrate the fundamental physics that dictates the final distribution of agglomerates in a casting. We have further extended this real time investigation to aluminum alloys using X-ray microscopy. A variety of interface-particle interactions will be discussed and how they alter the final properties of the composite. A demonstration of how a solid-liquid interface is distorted by nearby voids or particles, particle pushing or engulfment by the interface, formations of wormholes, Aggregation of particles, and particle-induced segregation of alloying elements will be presented.

Kaukler, William↗
DOE OSTI2020

Machine learning for collective variable discovery and enhanced sampling in biomolecular simulation

Classical molecular dynamics simulates the time evolution of molecular systems through the phase space spanned by the positions and velocities of the constituent atoms. Molecular-level thermodynamic, kinetic, and structural data extracted from the resulting trajectories provide valuable information for the understanding, engineering, and design of biological and molecular materials. The cost of simulating many-body atomic systems makes simulations of large molecules prohibitively expensive, and the high-dimensionality of the resulting trajectories presents a challenge for analysis. Driven by advances in algorithms, hardware, and data availability, there has been a flare of interest in recent years in the applications of machine learning – especially deep learning – to molecular simulation. Furthermore, these techniques have demonstrated great power and flexibility in both extracting mechanistic understanding of the important nonlinear collective variables governing the dynamics of a molecular system, and in furnishing good low-dimensional system representations with which to perform enhanced sampling or develop long-timescale dynamical models. It is the purpose of this article to introduce the key machine learning approaches, describe how they are married with statistical mechanical theory into domain-specific tools, and detail applications of these approaches in understanding and accelerating biomolecular simulation.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2022

Artificial neural network approach for multiphase segmentation of battery electrode nano-CT images

The segmentation of tomographic images of the battery electrode is a crucial processing step, which will have an additional impact on the results of material characterization and electrochemical simulation. However, manually labeling X-ray CT images (XCT) is time-consuming, and these XCT images are generally difficult to segment with histographical methods. We propose a deep learning approach with an asymmetrical depth encode-decoder convolutional neural network (CNN) for real-world battery material datasets. This network achieves high accuracy while requiring small amounts of labeled data and predicts a volume of billions voxel within few minutes. While applying supervised machine learning for segmenting real-world data, the ground truth is often absent. The results of segmentation are usually qualitatively justified by visual judgement. We try to unravel this fuzzy definition of segmentation quality by identifying the uncertainty due to the human bias diluted in the training data. Further CNN trainings using synthetic data show quantitative impact of such uncertainty on the determination of material’s properties. Nano-XCT datasets of various battery materials have been successfully segmented by training this neural network from scratch. We will also show that applying the transfer learning, which consists of reusing a well-trained network, can improve the accuracy of a similar dataset.

25 ENERGY STORAGE↗
NASA NTRS2010

Predicting Failure Progression and Failure Loads in Composite Open-Hole Tension Coupons

Failure types and failure loads in carbon-epoxy [45n/90n/-45n/0n]ms laminate coupons with central circular holes subjected to tensile load are simulated using progressive failure analysis (PFA) methodology. The progressive failure methodology is implemented using VUMAT subroutine within the ABAQUS(TradeMark)/Explicit nonlinear finite element code. The degradation model adopted in the present PFA methodology uses an instantaneous complete stress reduction (COSTR) approach to simulate damage at a material point when failure occurs. In-plane modeling parameters such as element size and shape are held constant in the finite element models, irrespective of laminate thickness and hole size, to predict failure loads and failure progression. Comparison to published test data indicates that this methodology accurately simulates brittle, pull-out and delamination failure types. The sensitivity of the failure progression and the failure load to analytical loading rates and solvers precision is demonstrated.

Arunkumar, Satyanarayana↗
NASA NTRS1977

Hypervelocity impact tests on Space Shuttle Orbiter thermal protection material

Hypervelocity impact tests were conducted to simulate the damage that meteoroids will produce in the Shuttle Orbiter leading edge structural subsystem material. The nature and extent of the damage is reported and the probability of encountering meteoroids with sufficient energy to produce such damage is discussed.

Humes, D. H.↗
NASA NTRS1980

Dielectric discharge characteristics in a two-electron simulation environment

The results of a series of experiments in which flux levels representative of the space electron environment were used are presented and compared to the results of high flux tests. The simulation approach was to partition the space electron spectrum into two parts, those electrons which do not penetrate a material and therefore contribute to charging and those which completely penetrate the material. The nonpenetrating electrons were simulated using 25 keV electrons and the penetrating electrons by 350 keV electrons. The materials included in this investigation were Kapton, optical solar reflectors (OSRs), and a ground test satellite surface potential monitor which contained Kapton, astroquart, OSRs and teflon.

Treadaway, M.↗
DOE OSTI2022

Extemporaneous Mechanochemistry: Shock-Wave-Induced Ultrafast Chemical Reactions Due to Intramolecular Strain Energy

We report regions of energy localization referred to as hotspots are known to govern shock initiation and the run-to-detonation in energetic materials. Mounting computational evidence points to accelerated chemistry in hotspots from large intramolecular strains induced via the interactions between the shock wave and microstructure. However, definite evidence mapping intramolecular strain to accelerated or altered chemical reactions has so far been elusive. From a large-scale reactive molecular dynamics simulation of the energetic material 1,3,5-triamino-2,4,6-trinitrobenzene, we map decomposition kinetics to molecular temperature and intramolecular strain energy prior to reaction. Both temperature and intramolecular strain are shown to accelerate chemical kinetics. A detailed analysis of the atomistic trajectory shows that intramolecular strain can induce a mechanochemical alteration of decomposition mechanisms. The results in this paper could inform continuum-level chemistry models to account for a wide range of mechanochemical effects.

36 MATERIALS SCIENCE↗