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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Effects of Mesoscale Eddies in the Active Mixed Layer: Test of the Parametrisation in Eddy Resolving Simulations

In eddy resolving simulations, we test a mixed layer mesoscale parametrisation, developed recently by Canuto and Dubovikov [Ocean Model., 2011, 39, 200-207]. With no adjustable parameters, the parametrisation yields the horizontal and vertical mesoscale fluxes in terms of coarse-resolution fields and eddy kinetic energy (EKE). We compare terms of the parametrisation diagnosed from coarse-grained fields with the eddy mesoscale fluxes diagnosed directly from the high resolution model. An expression for the EKE in terms of mean fields has also been found to get a closed parametrisation in terms of the mean fields only. In 40 numerical experiments we simulated two types of flows: idealised flows driven by baroclinic instabilities only, and more realistic flows, driven by wind and surface fluxes as well as by inflow-outflow. The diagnosed quasi-instantaneous horizontal and vertical mesoscale buoyancy fluxes (averaged over 1-2 degrees and 10 days) demonstrate a strong scatter typical for turbulent flows, however, the fluxes are positively correlated with the parametrisation with higher (0.5-0.74) correlations at the experiments with larger baroclinic radius Rossby. After being averaged over 3-4 months, diffusivities diagnosed from the eddy resolving simulations are consistent with the parametrisation for a broad range of parameters. Diagnosed vertical mesoscale fluxes restratify mixed layer and are in a good agreement with the parametrisation unless vertical turbulent mixing in the upper layer becomes strong enough in comparison with mesoscale advection. In the latter case, numerical simulations demonstrate that the deviation of the fluxes from the parametrisation is controlled by dimensionless parameter estimating the ratio of vertical turbulent mixing term to mesoscale advection. An analysis using a modified omega-equation reveals that the effects of the vertical mixing of vorticity is responsible for the two-three fold amplification of vertical mesoscale flux. Possible physical mechanisms, responsible for the amplification of vertical mesoscale flux are discussed.

simulation↗
DOE OSTI2023

Investigation of Best-Practices and Computationally Inexpensive Radiative Exchange Models for Discrete Element Method Modeling of Aluminosilicate Particles in Concentrating Solar Power Environments

Chemically inert, aluminosilicate based particles have been investigated as both a thermal transport and sensible energy storage medium for concentrating solar power facilities. These particles will experience a wide range of operating temperatures (300-1000 K) and handling conditions (dense to dilute falling particle curtains, dense granular flows, or dense structures), requiring specially-designed and optimized infrastructures. The relative influence of collisional and frictional interactions between particles varies based on temperature-dependent particulate properties and greatly impacts the bulk, granular flow behavior. These underlying physics are captured using discrete element method modeling tools. However, this modeling method is computationally expensive as each particle position and interaction is tracked during the simulation. These modeling methods are further complicated by introducing temperature-dependent particle properties, high-temperature radiative exchange, and directional irradiation sources experienced by granular flows in concentrating solar power environments. In this study, coupled experimental and numerical slump testing of aluminosilicate particles was performed and computationally efficient radiative exchange models were evaluated to establish best-practices for discrete element method models for concentrating solar power environments. The three particle types investigated included Carbobead HSP 30 /60, Carbobead CP 30/60, and Granusil 4030. Existing modeling limitations and computationally-efficient multi-modal heat transfer models were evaluated using Aspherix®, a commercial discrete element method software. High-temperature (< 1073 K) slump testing of aluminosilicate particles was performed to investigate the deviation between experimentally-observed and numerically-predicted angles of repose introduced by computation-time reduction practices including the relaxation of the particle elastic modulus and coarse-graining. Coarse-graining is used to use a single modeled particle that is representative of a collection of smaller particles, decreasing the computational cost at the expense of geometric accuracy. Additionally, relaxation of the elastic modulus is used to reduce computational time at the expense of an increased, modeled particle overlap. Prior studies have determined that aluminosilicate particles retain a high elastic modulus at high temperatures (< 1073 K), requiring small simulation timesteps to ensure resolved contact forces resemble appropriate solid mechanics. A parametric study was performed to evaluate the influence of computation time improvements on the deviation between experimental and modeled angle of repose across high temperatures < 1073 K. Additionally, numerical case studies were performed on candidate particle systems at varying porosities and temperatures. These studies were performed to investigate the influence of computationally-efficient radiative-exchange modeling methods coupled to Aspherix® on modeled accuracy and computation time. The recently-developed distance-based approximation was evaluated in estimating radiative exchange between particles and participating surfaces located in close proximity. The distance based approximation was developed to use tabulated estimates of the radiative distribution factor between individual particles and surfaces in close proximity (< 40 particle radii). These methods were expanded to the aluminosilicate particles of interest, including the influence of particle size distributions. To capture radiative exchange between particles and surfaces not in close proximity (> 40 particle radii) and to capture the absorption of directional irradiation from concentrating solar resources, a volumetrically-averaged radiative distribution factor was calculated between the modeled granular flow and surfaces using Monte Carlo ray-tracing for participating media. Volume-averaged absorption and scattering coefficients were predicted using a volumetric discretization of the modeled domain with monodisperse approximations based on geometric optics and experimentally-determined scattering phase functions for aluminosilicate particles.

14 SOLAR ENERGY↗
DOE OSTI2025

An adaptive, data-driven multiscale approach for dense granular flows

The accuracy of coarse-grained continuum models of dense granular flows is limited by the lack of high-fidelity closure models for granular rheology. One approach to addressing this issue, referred to as the hierarchical multiscale method, is to use a high-fidelity fine-grained model to compute the closure terms needed by the coarse-grained model. The difficulty with this approach is that the overall model can become computationally intractable due to the high computational cost of the high-fidelity model. In this work, we describe a multiscale modeling approach for dense granular flows that utilizes neural networks trained using high-fidelity discrete element method (DEM) simulations to approximate the constitutive granular rheology for a continuum incompressible flow model. Our approach leverages an ensemble of neural networks to estimate predictive uncertainty that allows us to determine whether the rheology at a given point is accurately represented by the neural network model. Additional DEM simulations are only performed when needed, minimizing the number of additional DEM simulations required when updating the rheology. This adaptive coupling significantly reduces the overall computational cost of the approach while controlling the error. In addition, the neural networks are customized to learn regularized rheological behavior to ensure well-posedness of the continuum solution. We first validate the approach using two-dimensional steady-state and decelerating inclined flows. We then demonstrate the efficiency of our approach by modeling three-dimensional sub-aerial granular column collapse for varying initial column aspect ratios, where our multiscale method compares well with the computationally expensive computational fluid dynamics (CFD)-DEM simulation.

Dense granular flows↗
DOE OSTI2024

Deciphering the Cofilin Oligomers via Intermolecular Disulfide Bond Formation: A Coarse-Grained Molecular Dynamics Approach to Understanding Cofilin’s Regulation on Actin Filaments

Cofilin, a key actin-binding protein, orchestrates the dynamics of the actomyosin network through its actin-severing activity and by promoting the recycling of actin monomers. Recent experimental work suggests that cofilin also forms functionally distinct oligomers through thiol post-translational modification (PTM) that encourages actin nucleation and assembly. Despite these advances, the structural conformations of cofilin oligomers that modulate actin activity remain elusive because there are combinatorial ways to oxidize thiols in cysteines to form disulfide bonds rapidly. This study employs molecular dynamics simulations to investigate human cofilin 1 as a case study for exploring cofilin dimers via disulfide bond formation. Using the free energy profiling, our simulations unveil a range of probable cofilin dimer structures not represented in current Protein Data Bank entries. These candidate dimers are characterized by their distinct population distributions and relative free energies. Of particular note is a dimer featuring an interface between cysteines 139 and 147 residues, which demonstrates stable free energy characteristics and intriguingly symmetrical geometry. In contrast, the experimentally proposed dimer structure exhibits a less stable free energy profile. Here, we also evaluate frustration quantification based on the energy landscape theory in the protein-protein interactions at the dimer interfaces. Notably, the 39-39 dimer configuration emerges as a promising candidate for forming cofilin tetramers, as substantiated by frustration analysis. Additionally, docking simulations with actin filaments further evaluate the stability of these cofilin dimer-actin complexes. Our findings thus offer a computational framework for understanding the role of thiol post-translational modification cofilin proteins in regulating oligomerization, and the subsequent cofilin-mediated actin dynamics in the actomyosin network.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Soft matter roadmap

Soft materials are usually defined as materials made of mesoscopic entities, often self-organised, sensitive to thermal fluctuations and to weak perturbations. Archetypal examples are colloids, polymers, amphiphiles, liquid crystals, foams. The importance of soft materials in everyday commodity products, as well as in technological applications, is enormous, and controlling or improving their properties is the focus of many efforts. From a fundamental perspective, the possibility of manipulating soft material properties, by tuning interactions between constituents and by applying external perturbations, gives rise to an almost unlimited variety in physical properties. Together with the relative ease to observe and characterise them, this renders soft matter systems powerful model systems to investigate statistical physics phenomena, many of them relevant as well to hard condensed matter systems. Understanding the emerging properties from mesoscale constituents still poses enormous challenges, which have stimulated a wealth of new experimental approaches, including the synthesis of new systems with, e.g. tailored self-assembling properties, or novel experimental techniques in imaging, scattering or rheology. Theoretical and numerical methods, and coarse-grained models, have become central to predict physical properties of soft materials, while computational approaches that also use machine learning tools are playing a progressively major role in many investigations. This Roadmap intends to give a broad overview of recent and possible future activities in the field of soft materials, with experts covering various developments and challenges in material synthesis and characterisation, instrumental, simulation and theoretical methods as well as general concepts.

36 MATERIALS SCIENCE↗
DOE OSTI2024

VOTCA: multiscale frameworks for quantum and classical simulations in soft matter

Many physical phenomena in liquids and soft matter are multiscale by nature and can involve processes with quantum and classical degrees of freedom occurring over a vast range of length- and timescales. Examples range from structure formation processes of complex polymers or even polymer blends (Svaneborg & Everaers, 2023) on the classical side to charge and energy transport and conversion processes (Lee et al., 2019) involving explicit electronic and, therefore, quantum information. The Versatile Object-oriented Toolkit for Coarse-graining Applications (VOTCA) provides multiscale frameworks built on a comprehensive set of methods for the development of classical coarse-grained potentials (VOTCA-CSG) as well as state-of-the art excited state electronic structure methods based on density-functional and many-body Green’s function theories, coupled in mixed quantum-classical models and used in kinetic network models (VOTCA-XTP).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Efficient Parallelization of Irregular Applications on GPU Architectures

With the enlarging computation capacity of general Graphics Processing Units (GPUs), leveraging GPUs to accelerate parallel applications has become a critical topic in academia and industry. However, a wide range of irregular applications with the computation-/memory-intensive nature cannot easily achieve high GPU utilization. The challenges mainly involve the following aspects: first, data dependence leads to coarse-grained kernel and inefficient parallelism; second, heavy GPU memory usage may cause frequent memory evictions and extra overhead of I/O; third, specific computation patterns produce memory redundancies; last, workload balance and data reusability conjunctly benefit the overall performance, but there may exist a dynamic trade-off between them. Targeting these challenges, this dissertation proposes multiple optimizations to accelerate two real-world applications: many-body correlation functions to simulate nuclear physics in a large-scale scientific system; the other is the eALS-based matrix factorization recommendation system. To accelerate the calculations of many-body correlation functions, this dissertation presents three frameworks in GPU memory management and multi-GPU scheduling. Firstly, an optimized systematic GPU memory management framework, MemHC, utilizes a series of new memory reduction designs in GPU memory allocation, CPU/GPU communications, and GPU memory oversubscription. Secondly, an enhanced multi-GPU scheduling framework, MICCO, particularly by taking both data dimension (e.g., data reuse and data eviction) and computation dimension into account. MICCO designs a heuristic scheduling algorithm and a machine learning-based regression model to generate the optimal settings of a proposed new concept to manage the trade-off. Thirdly, a locality-aware multi-GPU scheduling framework. This scheduler leverages pipeline batch generation with a looking-ahead strategy by building local dependency graphs for memory transfer reduction and better data reuse, achieving up to 79.92% memory cost reduction and 1.67x speedup. To parallelize the eALS-based recommendation system, this dissertation proposes an efficient CPU/GPU heterogeneous recommendation system, HEALS. HEALS employs newly designed architecture-adaptive data formats to achieve load balance and good data locality on CPU and GPU. To mitigate the data dependence, HEALS presents a CPU/GPU collaboration model for both task parallelism and data parallelism with multiple kernel computation optimizations. In summary, this dissertation efficiently accelerates two typical irregular applications on GPUs by building four frameworks, including CPU/GPU collaboration, GPU memory management, and multi-GPU scheduling.

Wang, Qihan↗
DOE OSTI2024

Peridynamic Models for Random Media Found by Coarse Graining

Using coarse graining, the upscaled mechanical properties of a solid with small scale heterogeneities are derived. The method maps internal forces at the small scale onto peridynamic bond forces in the coarse grained mesh. These upscaled bond forces are used to calibrate a peridynamic material model with position-dependent parameters. These parameters incorporate mesoscale variations in the statistics of the small scale system. The upscaled peridynamic model can have a much coarser discretization than the original small scale model, allowing larger scale simulations to be performed efficiently. The convergence properties of the method are investigated for representative random microstructures. In conclusion, a bond breakage criterion for the upscaled peridynamic material model is also demonstrated.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Quantum real-time evolution using tensor renormalization group methods

We introduce an approach for approximate real-time evolution of quantum systems using tensor renormalization group (TRG) methods originally developed for imaginary time. We use higher-order TRG to generate a coarse-grained time evolution operator for a 1+1⁢D transverse Ising model with a longitudinal field. We show that the standard tensor norm used for the singular value decomposition-based truncation is degenerate and propose an alternate method to discriminate. We show that it is effective and efficient in evolving Gaussian wave packets for one and two particles in the disordered phase, while ordered phase behavior is more challenging to capture. We compare our algorithm with local simulators for universal quantum computers and discuss possible benchmarking in the near future.

lattice gauge theory↗
DOE OSTI2026

Can classical DEM simultaneously capture compressibility and flowability of milled biomass?

Accurate prediction of the rheological behavior of biomass is essential for the design and operation of hoppers, feeders, and storage systems in biorefineries. This study examines whether the classical, coarse-grained discrete element method (DEM) formulation can simultaneously reproduce the compressibility and flowability of milled herbaceous biomass, using Miscanthus × giganteus as a representative material. The model represents particles as rigid spheres interacting through Hertz-Mindlin elastic-frictional contacts augmented with an area-dependent cohesion term. Laboratory cyclic compression and wedge-shaped hopper discharge experiments were used as calibration benchmarks. Although the model can independently reproduce each behavior by appropriately tuning particle Young's modulus E and cohesion energy density k, an extensive parametric investigation comprising more than 600 simulations reveals that the optimal parameter regions for compression and hopper flow are distinct and non-overlapping in (E, k) space. Surrogate surface analysis further shows that the corresponding objective-function valleys exhibit similar trends but are approximately parallel and spatially offset, precluding a unified calibration within the explored domain. Sensitivity analysis indicates that compressibility is governed predominantly by stiffness and cohesion, whereas the slope of the mass flow rate-opening relation in hopper discharge is primarily controlled by tangential friction. Extensions incorporating particle size distribution and clumped-sphere representations do not eliminate the incompatibility. These results systematically reveal, for the first time, the structural limitation of simplified DEM formulations in representing biomass rheological behavior, underscoring the necessity for models incorporating additional physical mechanisms, such as particle deformability or enhanced interlocking, to achieve unified predictive capability for biomass handling behavior.

09 BIOMASS FUELS↗
DOE OSTI2024

Learning stochastic dynamics and predicting emergent behavior using transformers

We show that a neural network originally designed for language processing can learn the dynamical rules of a stochastic system by observation of a single dynamical trajectory of the system, and can accurately predict its emergent behavior under conditions not observed during training. We consider a lattice model of active matter undergoing continuous-time Monte Carlo dynamics, simulated at a density at which its steady state comprises small, dispersed clusters. We train a neural network called a transformer on a single trajectory of the model. The transformer, which we show has the capacity to represent dynamical rules that are numerous and nonlocal, learns that the dynamics of this model consists of a small number of processes. Forward-propagated trajectories of the trained transformer, at densities not encountered during training, exhibit motility-induced phase separation and so predict the existence of a nonequilibrium phase transition. Transformers have the flexibility to learn dynamical rules from observation without explicit enumeration of rates or coarse-graining of configuration space, and so the procedure used here can be applied to a wide range of physical systems, including those with large and complex dynamical generators.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Data-driven modeling of dislocation mobility from atomistics using physics-informed machine learning

Dislocation mobility, which dictates the response of dislocations to an applied stress, is a fundamental property of crystalline materials that governs the evolution of plastic deformation. Traditional approaches for deriving mobility laws rely on phenomenological models of the underlying physics, whose free parameters are in turn fitted to a small number of intuition-driven atomic scale simulations under varying conditions of temperature and stress. This tedious and time-consuming approach becomes particularly cumbersome for materials with complex dependencies on stress, temperature, and local environment, such as body-centered cubic crystals (BCC) metals and alloys. In this paper, we present a novel, uncertainty quantification-driven active learning paradigm for learning dislocation mobility laws from automated high-throughput large-scale molecular dynamics simulations, using Graph Neural Networks (GNN) with a physics-informed architecture. We demonstrate that this Physics-informed Graph Neural Network (PI-GNN) framework captures the underlying physics more accurately compared to existing phenomenological mobility laws in BCC metals.

36 MATERIALS SCIENCE↗
NASA NTRS1998

An Assessment of the Axial and Radial Dilation of a DPIMS Tantalum Cartridge for Space Shuttle Flight Experiments

Ground-based heat treatment tests are planned on an argon gas-filled tantalum cartridge developed as pan of a Diffusion Processes in Molten Semiconductors (DPIMS) experiment conducted on NASA's Space Shuttle. The possibility that the cartridge may creep during testing and touch the furnace walls is of real concern in this program. The present paper discusses the results of calculations performed to evaluate this possibility. Deformation mechanism maps were constructed using literature data in order to identify the creep mechanism dominant under the appropriate stresses and temperatures corresponding to the test conditions. These results showed that power-law creep was dominant when the grain size of the material exceeded 55 gm but Coble creep was the important mechanism below this value of grain size. Finite element analysis was used to analyze the heat treatment tWs assuming a furnace run away condition (which is a worst case scenario) using the appropriate creep parameters corresponding to grain sizes of 1 and 100 gm. Calculations were also conducted to simulate the effect of an initial 3 tilt of the cartridge assembly, the maximum possible tilt angle. The von Mises stress and su-ain distributions were calculated assuming that the cartridge was fixed at one end as it was heated from ambient temperature to 1823 K in 1.42 h, maintained at 1823 K for 9.5 h and then further heated to an over temperature condition of 2028 K in 0.3 h. The inelastic axial and radial displacements of the cartridge walls were evaluated by resolving the von Mises strain along the corresponding directions. These calculations reveal that the maximum axial and radial displacements are expected to be about 2.9 and 0.25 mm, respectively, for both fine and coarse-grained materials at 2028 K. It was determined that these displacements occur during heat-up to temperature and creep of the cartridge is likely to be relatively insignificant irrespective of grain size. Furthermore, with a 3' tilt of the cartridge, the deflection is increased by only 0.39 gm which is negligible. Since the gap between the furnace heating elements and the cartridge is about 7.5 mm and less than the maximum radial dilation of 0.25 mm at 2028 K, it is concluded that the cartridge is unlikely to touch the furnace walls during the experiments.

Raj, S.V.↗
NASA NTRS2001

Radiation breakage of DNA: a model based on random-walk chromatin structure

Monte Carlo computer software, called DNAbreak, has recently been developed to analyze observed non-random clustering of DNA double strand breaks in chromatin after exposure to densely ionizing radiation. The software models coarse-grained configurations of chromatin and radiation tracks, small-scale details being suppressed in order to obtain statistical results for larger scales, up to the size of a whole chromosome. We here give an analytic counterpart of the numerical model, useful for benchmarks, for elucidating the numerical results, for analyzing the assumptions of a more general but less mechanistic "randomly-located-clusters" formalism, and, potentially, for speeding up the calculations. The equations characterize multi-track DNA fragment-size distributions in terms of one-track action; an important step in extrapolating high-dose laboratory results to the much lower doses of main interest in environmental or occupational risk estimation. The approach can utilize the experimental information on DNA fragment-size distributions to draw inferences about large-scale chromatin geometry during cell-cycle interphase.

Chromatin/radiation effects↗
NASA NTRS1992

Optical and TEM study of shock metamorphism from the Sedan test site

Thus far, detailed petrologic studies of shock metamorphism have been performed on samples recovered from laboratory experiments and on a few natural impactites. The loading history of these samples is quite different: In particular, laboratory experiments spend only a short time (less than 1 microsec) at peak pressure, whereas natural impactites may have stress pulses from 0.1 - 1 ms. On the other hand, laboratory experiments have known stress histories; natural impactites do not. Natural samples are also subjected to thousands or millions of years of postshock annealing and/or weathering. A useful intermediate case is that of nuclear detonation. Stress pulses for these events can reach 0.1 ms or higher, and samples are obtained in pristine condition. All three types of loading produce stresses of hundreds of kilobars. Samples studied were taken from the Sedan nulcear test site, and consist of a coarse-grained granodiorite containing quartz, K-feldspar, cordierite, and hornblende. Samples were studied optically in this section, then were thinned with an ion mill and studied by transmission electron microscopy (TEM). Optically, quartz and K-feldspar displayed numerous sets of planar deformation features (PDF's) identical to the nondecorated PDF's seen in laboratory samples and many natural impactites. TEM study showed that the PDF's in quartz and feldspar corresponded to densely packed wide transformation lamellae identical to those described in laboratory studies. The transformation lamellae in both minerals were amorphous, with no sign of high-pressure phases. In the case of K-feldspar only, narrow sublamellae extended outward from some wide lamellae. Quartz, which was more abundant and studied more extensively, contained no shock-induced dislocations. Some planar features were also seen in cordierite, but could not be identified due to rapid beam damage. No shock defects were seen in hornblende in TEM. The shock-induced defects present at the Sedan site are very similar to those seen in shock recovery experiments, and also to those present at certain natural events (e.g., Meteor Crater). This suggests that shock deformation in quartz is not strongly dependent on shock pulse duration, and that laboratory recovery experiments are useful simulations of natural impact events.

Gratz, A. J.↗
DOE OSTI2023

Grain boundary effects in high-temperature liquid-metal dealloying: a multi-phase field study

Abstract A multi-phase field model is employed to study the microstructural evolution of an alloy undergoing liquid dealloying, specifically considering the role of grain boundaries. A semi-implicit time-stepping algorithm using spectral methods is implemented, which enables simulating large 2D and 3D domains over long time scales while still maintaining a realistic interfacial thickness. Simulations reveal a mechanism of coupled grain–boundary migration to maintain equilibrium contact angles with the topologically complex solid–liquid interface, which locally accelerates diffusion-coupled growth of a liquid channel into the precursor. This mechanism asymmetrically disrupts the ligament connectivity of the dealloyed structure in qualitative agreement with published experimental observations. The grain boundary migration-assisted corrosion channels form even for precursors with small amounts of the dissolving alloy species, below the parting limit . The activation of this grain boundary dealloying mechanism depends strongly on grain boundary mobility.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Arbitrarily accurate, nonparametric coarse graining with Markov renewal processes and the Mori–Zwanzig formulation

Stochastic dynamics, such as molecular dynamics, are important in many scientific applications. However, summarizing and analyzing the results of such simulations is often challenging due to the high dimension in which simulations are carried out and, consequently, due to the very large amount of data that are typically generated. Coarse graining is a popular technique for addressing this problem by providing compact and expressive representations. Coarse graining, however, potentially comes at the cost of accuracy, as dynamical information is, in general, lost when projecting the problem in a lower-dimensional space. This article shows how to eliminate coarse-graining error using two key ideas. First, we represent coarse-grained dynamics as a Markov renewal process. Second, we outline a data-driven, non-parametric Mori–Zwanzig approach for computing jump times of the renewal process. Numerical tests on a small protein illustrate the method.

36 MATERIALS SCIENCE↗
NASA NTRS1992

Lunar resources: Oxygen from rocks and soil

The first set of hydrogen reduction experiments to use actual lunar material was recently completed. The sample, 70035, is a coarse-grained vesicular basalt containing 18.46 wt. percent FeO and 12.97 wt. percent TiO2. The mineralogy includes pyroxene, ilmenite, plagioclase, and minor olivine. The sample was crushed to a grain size of less than 500 microns. The crushed basalt was reduced with hydrogen in seven tests at temperatures of 900-1050 C and pressures of 1-10 atm for 30-60 minutes. A capacitance probe, measuring the dew point of the gas stream, was used to follow reaction progress. Experiments were also conducted using a terrestrial basalt similar to some lunar mare samples. Minnesota Lunar Simulant (MLS-1) contains 13.29 wt. percent FeO, 2.96 wt. percent Fe2O3, and 6.56 wt. percent TiO2. The major minerals include plagioclase, pyroxene, olivine, ilmenite, and magnetite. The rock was ground and seived, and experiments were run on the less than 74- and 500-1168-micron fractions. Experiments were also conducted on less than 74-micron powders of olivine, pyroxene, synthetic ilmenite, and TiO2. The terrestrial rock and mineral samples were reduced with flowing hydrogen at 1100 C in a microbalance furnace, with reaction progress monitored by weight loss. Experiments were run at atmospheric pressure for durations of 3-4 hr. Solid samples from both sets of experiments were analyzed by Mossbauer spectroscopy, petrographic microscopy, scanning electron microscopy, tunneling electron microscopy, and x-ray diffraction. Apollo 17 soil 78221 was examined for evidence of natural reduction in the lunar environment. This sample was chosen based on its high maturity level (I sub s/FeO = 93.0). The FeO content is 11.68 wt. percent and the TiO2 content is 3.84 wt. percent. A polished thin section of the 90-150 micron size fraction was analyzed by petrographic microscopy and scanning electron microscopy.

Allen, C. C.↗