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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Multiclustering needlet ILC for CMB B -mode component separation

ABSTRACT The Cosmic Microwave Background (CMB) primordial B-mode signal is predicted to be much lower than the polarized Galactic emission (foregrounds) in any region of the sky pointing to the need for sophisticated component separation methods. Among them, the blind Needlet Internal Linear Combination (NILC) has great relevance given our current poor knowledge of the B-mode foregrounds. However, the expected level of spatial variability of the foreground spectral properties complicates the NILC subtraction of the Galactic contamination. We therefore propose a novel extension of the NILC approach, the Multiclustering NILC (MC-NILC), which performs NILC variance minimization on separate regions of the sky (clusters) properly chosen to have similar spectral properties of the B-mode Galactic emission within them. Clusters are identified thresholding either the ratio of simulated foregrounds-only B modes (ideal case) or the one of cleaned templates of Galactic emission obtained from realistic simulations. In this work we present an application of MC-NILC to the future LiteBIRD satellite, which targets the observation of both reionization and recombination peaks of the primordial B-mode angular power spectrum with a total error on the tensor-to-scalar ratio δr < 0.001. We show that MC-NILC provides a CMB solution with residual foreground and noise contamination that is significantly lower than the NILC one and the primordial signal targeted by LiteBIRD at all angular scales for the ideal case and at the reionization peak for a realistic ratio. Thus, MC-NILC will represent a powerful method to mitigate B-mode foregrounds for future CMB polarization experiments.

Astronomy & Astrophysics↗
NASA NTRS2010

NICMOS Spectroscopy of HD 189733b

Spectral features corresponding to methane and water opacity were reported based on transmission spectroscopy of HD 189733b with Hubble/NICMOS. Recently, these data, and a similar data set for XO-1b, have been reexamined in Gibson et al. 2010, who claim they cannot reliably reproduce prior results. We examine the methods used by the Gibson team and identify two specific issues that could act to increase the formal uncertainties and to create instability in the minimization process. This would also be consistent with the GPA10 finding that they could not identify a way to select among the several instrument models they constructed. In the case of XO-1b, the Gibson team significantly changed the way in which the instrument model is defined (both with respect to the three approaches they used for HD 189733b, and the approach used by previous authors); this change, which omits the effect of the spectrum position on the detector, makes direct intercomparison of results difficult. In the experience of our group, the position of the spectrum on the detector is an important element of the instrument model because of the significant residual structure in the NICMOS spectral flat field. The approach of changing instrument models significantly complicates understanding the data reduction process and interpreting the results. Our team favors establishing a consistent method of handling NICMOS instrument systematic errors and applying it uniformly to data sets.

infrared↗
DOE OSTI2021

Hotspot Coevolution Is a Key Identifier of Near-Native Protein Complexes

Protein–protein interactions play a key role in mediating numerous biological functions, with more than half the proteins in living organisms existing as either homo- or hetero-oligomeric assemblies. Protein subunits that form oligomers minimize the free energy of the complex, but exhaustive computational search-based docking methods have not comprehensively addressed the challenge of distinguishing a natively bound complex from non-native forms. Current protein docking approaches address this problem by sampling multiple binding modes in proteins and scoring each mode, with the lowest-energy (or highest scoring) binding mode being regarded as a near-native complex. However, high-scoring modes often match poorly with the true bound form, suggesting a need for improvement of the scoring function. Here in this study, we propose a scoring function, KFC-E, that accounts for both conservation and coevolution of putative binding hotspot residues at protein–protein interfaces. We tested KFC-E on four benchmark sets of unbound examples and two benchmark sets of bound examples, with the results demonstrating a clear improvement over scores that examine conservation and coevolution across the entire interface.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2018

Reflective Coatings for the Future X-Ray Mirror Substrates

We present the development of the reflective coating by magnetron sputtering deposition onto precisely-fabricated thin X-ray mirrors. Our goal is to remove distortion induced by the coating and then keep their surface pro les. We first addressed the uniform coating to minimize the distortion by introducing a mask to control the spatial distribution of the coating thickness. The uniformity was finally achieved within 1%. We next tried a platinum single-layer coating on a glass substrate with a dimension of 200 mm 125 mm. The distortion caused by the frontside coating with a thickness of 320 A was found to be at most 1 m, smaller than the previous results obtained from the non-uniform coating. We then carried out the platinum coating with the same amount of the thickness on the backside surface of the glass substrate. The surface pro le of the glass substrate was fully recovered, indicating that the residual stress was successfully balanced by the backside coating. Furthermore, we tried to an iridium single-layer coating with a thickness of 150 Aon the silicon mirrors. The frontside coating caused the degradation of the imaging quality by 7:5 arcsec in half-power width. However, the backside coating with the same amount of the thickness reduced this degradation to be 3:4 arcsec. Finally, an additional backside coating with a thickness of 100 A and the annealing to relax the residual stress were found to eliminate the distortion completely; the final degradation of the imaging quality was only 0:4 arcsec.

Mori, Hideyuki↗
DOE OSTI2025

Characterization of the Cherenkov Photon Background for Low-noise Silicon Detectors in Space

Future space observatories that seek to perform imaging and spectroscopy of faint astronomical sources will require ultra-low-noise detectors that are sensitive over a broad wavelength range. Silicon charge-coupled devices (CCDs), such as EMCCDs, skipper CCDs, multi-amplifier sensing CCDs, and single-electron sensitive read out CCDs have demonstrated the ability to detect and measure single photons from X-ray energies to near the silicon band gap (∼1.1 μm), making them candidate technologies for this application. Here, in this context, we study a relatively unexplored source of low-energy background coming from Cherenkov radiation produced by energetic cosmic rays traversing a silicon detector. We present a model for Cherenkov photon production and absorption that is calibrated to laboratory data, and we use this model to characterize the residual background rate for ultra-low-noise silicon detectors in space. We study how the Cherenkov background rate depends on detector thickness, variations in solar activity, and the contribution of heavy cosmic ray species (Z > 2). We find that for thick silicon detectors, such as those required to achieve high quantum efficiency at long wavelengths, the rate of cosmic-ray-induced Cherenkov photon production is comparable to other detector and astrophysical backgrounds. We apply our Cherenkov background model to simulated spectroscopic observations of extra-solar planets, and we find that thick detectors continue to outperform their thinner counterparts at longer wavelengths despite a larger Cherenkov background rate. Furthermore, we find that minimal masking of cosmic-ray tracks continues to maximize the signal-to-noise ratio of very faint sources despite the existence of extended halos of Cherenkov photons.

Astronomical detectors↗
DOE OSTI2026

Monitoring of Liquid Metal Reactor Heater Zones with Recurrent Neural Network Learning of Temperature Time Series

Advanced high-temperature fluid reactors (ARs), such as sodium fast reactors (SFRs) and molten salt cooled reactors (MSCRs) utilize high-temperature fluids at ambient pressure. To melt the fluid during reactor startup and prevent fluid freezing during cooldown, the thermal–hydraulic systems of such ARs include heater zones consisting of specific heaters with controllers, temperature sensors, and thermal insulation. The failure of heater zones due to insulation material degradation or improper installation, resulting in parasitic heat losses, can lead to fluid freezing. The detection of faults using a heat-transfer model is difficult because of a lack of knowledge of the experimental details. Data-driven machine learning of heater zone temperature time series offers a viable alternative. In this study, we benchmarked the performance of recurrent neural networks (RNNs) in an analysis of heat-up transient temperature time series of heater zones installed on a liquid sodium vessel. The RNN models include long short-term memory (LSTM) and gated recurrent unit (GRU) networks, as well as their bi-directional variants, BiLSTM and BiGRU. Anomalous temperature points were designated using a percentile-based threshold applied to residual fluctuations in the detrended temperature time series. Additionally, the impact of the exponentially weighted moving average (EWMA) method on detection accuracy was examined. The RNN models’ performance was assessed using precision, recall, and F 1 score metrics. Results demonstrated that RNN models effectively detect anomalies in temperature time series with the best models for each heater zone achieving F 1 scores of over 93%. To explain the variations in RNN model performance across different heater zones, we used Kullback–Leibler (KL) divergence to quantify the relative entropy between training and testing data, and the Detrended Fluctuation Analysis (DFA) to assess long-range temporal correlations. For datasets with strong long-range correlations and minimal relative entropy between training and testing data, GRU is the best-performing model. When the data exhibits weaker long-term correlations and a significant relative entropy between training and testing distributions, BiGRU shows the best performance. For the data sets with intermediate values of both KL divergence and DFA, the best performance is obtained with LSTM and BiLSTM, respectively.

gated recurrent unit↗
NASA NTRS1999

Solving Upwind-Biased Discretizations: Multigrid Solver Using Semicoarsening - 2

This paper studies a novel multigrid approach to the solution for a second order upwind biased discretization of the convection equation in two dimensions. This approach is based on semi-coarsening and well balanced explicit correction terms added to coarse-grid operators to maintain on coarse-grid the same cross-characteristic interaction as on the target (fine) grid. Colored relaxation schemes are used on all the levels allowing a very efficient parallel implementation. The results of the numerical tests can be summarized as follows: 1) The residual asymptotic convergence rate of the proposed V(0, 2) multigrid cycle is about 3 per cycle. This convergence rate far surpasses the theoretical limit (4/3) predicted for standard multigrid algorithms using full coarsening. The reported efficiency does not deteriorate with increasing the cycle, depth (number of levels) and/or refining the target-grid mesh spacing. 2) The full multi-grid algorithm (FMG) with two V(0, 2) cycles on the target grid and just one V(0, 2) cycle on all the coarse grids always provides an approximate solution with the algebraic error less than the discretization error. Estimates of the total work in the FMG algorithm are ranged between 18 and 30 minimal work units (depending on the target (discretizatioin). Thus, the overall efficiency of the FMG solver closely approaches (if does not achieve) the goal of the textbook multigrid efficiency. 3) A novel approach to deriving a discrete solution approximating the true continuous solution with a relative accuracy given in advance is developed. An adaptive multigrid algorithm (AMA) using comparison of the solutions on two successive target grids to estimate the accuracy of the current target-grid solution is defined. A desired relative accuracy is accepted as an input parameter. The final target grid on which this accuracy can be achieved is chosen automatically in the solution process. the actual relative accuracy of the discrete solution approximation obtained by AMA is always better than the required accuracy; the computational complexity of the AMA algorithm is (nearly) optimal (comparable with the complexity of the FMG algorithm applied to solve the problem on the optimally spaced target grid).

Diskin, Boris↗
NASA NTRS1996

Distortion Representation of Forecast Errors for Model Skill Assessment and Objective Analysis

We study a novel characterization of errors for numerical weather predictions. In its simplest form we decompose the error into a part attributable to phase errors and a remainder. The phase error is represented in the same fashion as a velocity field and will be required to vary slowly and smoothly with position. A general distortion representation allows for the displacement and a bias correction of forecast anomalies. In brief, the distortion is determined by minimizing the objective function by varying the displacement and bias correction fields. In the present project we use a global or hemispheric domain, and spherical harmonics to represent these fields. In this project we are initially focusing on the assessment application, restricted to a realistic but univariate 2-dimensional situation. Specifically we study the forecast errors of the 500 hPa geopotential height field for forecasts of the short and medium range. The forecasts are those of the Goddard Earth Observing System data assimilation system. Results presented show that the methodology works, that a large part of the total error may be explained by a distortion limited to triangular truncation at wavenumber 10, and that the remaining residual error contains mostly small spatial scales.

Hoffman, Ross N.↗
DOE OSTI2020

Assessment of the feedstock supply for siting single- and multiple-feedstock biorefineries in the USA and identification of prevalent feedstocks

An integrated multi-feedstock bioenergy (i.e., biofuel, biopower, or bioproduct) supply system has potential to reduce biomass supply system uncertainties and costs. This study identifies optimal configurations of multi-feedstock biomass-to-biorefinery supply chains and pertinent feedstock combinations based on spatial distribution of feedstock and lowest delivered cost to the biorefinery. We used the Supply Characterization Model (SCM) to allocate feedstock supplies to candidate biorefinery facilities. Model runs were performed for herbaceous energy crops, agriculture residue, and woody biomass available in 2017, 2022, 2025, and 2030 as estimated by the Policy Analysis System (POLYSYS) and Forest Sustainable and Economic Analysis Model (ForSEAM) models. Three feedstock supply scenarios were compared: (a) an herbaceous scenario: switchgrass, miscanthus, biosorghum, and corn stover; (b) a woody scenario: coppice wood, noncoppice wood, whole trees, and forestry residues, and (c) a mixed scenario: a combination of all feedstocks in herbaceous and woody scenarios. By 2030 the analyses predicted that 323, 168, and 473 biorefineries were sited in the herbaceous, woody, and mixed scenario, respectively, in the conterminous USA. Feedstock mixes supplied to the biorefineries were mostly dominated by a single feedstock. The most prominent feedstock mixes identified were: (1) switchgrass and miscanthus; (2) coppice and noncoppice wood; and (3) coppice wood, noncoppice wood, switchgrass and miscanthus. Biorefineries using multi-feedstock would be beneficial for growth of bioeconomy, however flexible and cost-effective conversion platforms should be developed to efficiently utilize multiple feedstocks. This analysis identifies biorefinery locations and feedstock supply mixes while minimizing delivered feedstock costs based on spatial and temporal feedstock availability.

09 BIOMASS FUELS↗
DOE OSTI2024

Stereoselective recognition of morphine enantiomers by μ -opioid receptor

Stereospecific recognition of chiral molecules plays a crucial role in biological systems. The μ-opioid receptor (MOR) exhibits binding affinity towards (-)-morphine, a well-established gold standard in pain management, while it shows minimal binding affinity for the (+)-morphine enantiomer, resulting in a lack of analgesic activity. Understanding how MOR stereoselectively recognizes morphine enantiomers has remained a puzzle in neuroscience and pharmacology for over half-a-century due to the lack of direct observation techniques. To unravel this mystery, we constructed the binding and unbinding processes of morphine enantiomers with MOR via molecular dynamics simulations to investigate the thermodynamics and kinetics governing MOR's stereoselective recognition of morphine enantiomers. Our findings reveal that the binding of (-)-morphine stabilizes MOR in its activated state, exhibiting a deep energy well and a prolonged residence time. In contrast, (+)-morphine fails to sustain the activation state of MOR. Furthermore, the results suggest that specific residues, namely D114 2.50 and D147 3.32 , are deprotonated in the active state of MOR bound to (-)-morphine. This work highlights that the selectivity in molecular recognition goes beyond binding affinities, extending into the realm of residence time.

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

Phonon-informed Neural Thermal Scattering (NeTS) Optimization for Crystalline Graphite and Beryllium Metal

Fast neutrons born from fission lose energy through scattering interactions in the process of slowing-down. As neutrons thermalize to the order of $k$ $b$ $T$ (where $k$ $b$ is the Boltzmann constant, and $T$ is the temperature of the medium), their de Broglie wavelength and energy approaches the order of inter-atomic spacing and quantized lattice vibrations, i.e., phonons. At thermal energies, the thermal scattering law (TSL), i.e., $S$($α, β$), captures crystal binding contributions to the total reaction rate, or cross section. This dimensionless material property describes the energy ($β$) and momentum ($α$) exchanges available in a medium. Currently, $S$($α, β$) is evaluated in the Full Law Analysis Scattering System Hub (FLASSH) code for discrete inputs and stored as ENDF/B File 7 for 0-phonon elastic (MT 2) and n-phonon inelastic (MT 4) processes. Further processing recasts $S$($α, β$) into cumulative distribution functions for sampling post-collision scattering kinematics. In practice, interpolation schemes are employed to access data between tabulated values. An improvement to this juncture of the nuclear data pipeline is supplying cross sections on-the-fly (OTF), as has been developed for the un-resolved resonance region to minimize non-physical interpolation errors. This capability may improve simulation accuracy for accident and transient analyses, where rapidly varying changes in temperature and pressure are difficult to predict beforehand. To do so, deep artificial neural networks (ANNs) can be employed which collapse non-linear, complex data into a lightweight dictionary of neural weights and biases. This has been successfully demonstrated for the hydrogen in light water $S$($α, β$) dataset in the form of a Neural Thermal Scattering (NeTS) module. In this work, the NeTS framework is extended to consider the impact of material-dependent dynamical features on optimal neural pre-processing and architecture design decisions, such as number of neurons per hidden layer, residual skip connections and neural depth. New NeTS modules for crystalline graphite and beryllium metal illuminate a novel correlation between dynamical nonlinearity and optimal neural parametrization when deploying $S$($α, β$) on-the-fly.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

ISR Restoration Methods and Applicability to Groundwater Remediation at Abandoned Uranium Mills - 20342

There are numerous abandoned uranium mining and mill sites in the southwestern US. Surface remediation was undertaken in the 1980's and 90's to cover mill tailings and minimize infiltration of meteoric precipitation into the subsurface These actions have reduced the availability of tailings contaminants but many of these sites have persistent groundwater contamination, primarily due to the disposal of tailings and process fluids in unlined impoundments during the mill's operational life. Active in situ recovery (ISR) uranium mining and wellfield restoration practices are examined to determine the factors that contribute to the success of groundwater restoration. Hydrologic characteristics, geochemistry, ore body quality, regulatory requirements and the aboveground components of uranium production and water treatment are considered. While there is no such thing as a typical abandoned mill site, the potential use of ISR restoration methods is evaluated for viability as a remedial alternative to mine and mill sites that have residual contaminated groundwater plumes. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Lignin-enzyme interaction: A roadblock for efficient enzymatic hydrolysis of lignocellulosics

Efficiently producing second-generation biofuels from biomass is of strategic significance and meets sustainability targets, but it remains a long-term challenge due to the existence of biomass recalcitrance. Lignin contributes significantly to biomass recalcitrance by physically limiting the access of enzymes to carbohydrates, and this could be partially overcome by applying a pretreatment step to directly target lignin. However, lignin typically cannot be completely removed, and its structure is also significantly altered during the pretreatment. As a result, lignin residue in the pretreated materials still significantly hindered a complete conversion of carbohydrate to its monosugars by interacting with cellulase enzymes. The non-productive adsorption driven by hydrophobic, electrostatic, and/or hydrogen bonding interactions is widely considered as the major mechanism of action governing the unfavored lignin-enzyme interaction. One could argue this type of interaction between lignin residue and the activated enzymes is the major roadblock for efficient enzymatic hydrolysis of pretreated lignocellulosics. To alleviate the negative effects of lignin on enzyme performance, a deep understanding of lignin structural transformation upon different types of pretreatments as well as how and where does lignin bind to enzymes are prerequisites. In the last decade, the progress toward a fundamental understanding of lignin-enzyme interaction, structural characterization of lignin during pretreatment and/or conformation change of enzyme during hydrolysis is resulting in advances in the development of methodologies to mitigate the negative effect of lignin. Here in this review, the lignin structural transformation upon different types of pretreatments and the inhibition mechanism of lignin in the bioconversion of lignocellulose to bioethanol are summarized. Some technologies to minimize the adverse impact of lignin on the enzymatic hydrolysis, including chemical modification of lignin, adding blocking additives, and post-treatment to remove lignin were also introduced. The production of liquid biofuels from lignocellulosic biomass has shown great environmental benefits such as reducing greenhouse gas emissions and mitigate climate change. By addressing the root causes of lignin-enzyme interaction and how to retard this interaction, it is our hope that this comprehensive review will pave the way for significantly reducing the high cost associated with the enzymatic hydrolysis process, and ultimately achieving a cost-effective and sustainable biorefinery system.

09 BIOMASS FUELS↗
DOE OSTI2024

Persistent global greening over the last four decades using novel long-term vegetation index data with enhanced temporal consistency

Advanced Very High-Resolution Radiometer (AVHRR) satellite observations have provided the longest global daily records from 1980s, but the remaining temporal inconsistency in vegetation index datasets has hindered reliable assessment of vegetation greenness trends. To tackle this, we generated novel global long-term Normalized Difference Vegetation Index (NDVI) and Near-Infrared Reflectance of vegetation (NIRv) datasets derived from AVHRR and Moderate Resolution Imaging Spectroradiometer (MODIS). We addressed residual temporal inconsistency through three-step post processing including cross-sensor calibration among AVHRR sensors, orbital drifting correction for AVHRR sensors, and machine learning-based harmonization between AVHRR and MODIS. After applying each processing step, we confirmed the enhanced temporal consistency in terms of detrended anomaly, trend and interannual variability of NDVI and NIRv at calibration sites. Our refined NDVI and NIRv datasets showed a persistent global greening trend over the last four decades (NDVI: 0.0008 yr -1 ; NIRv: 0.0003 yr -1 ), contrasting with those without the three processing steps that showed rapid greening trends before 2000 (NDVI: 0.0017 yr -1 ; NIRv: 0.0008 yr -1 ) and weakened greening trends after 2000 (NDVI: 0.0004 yr -1 ; NIRv: 0.0001 yr -1 ). These findings highlight the importance of minimizing temporal inconsistency in long-term vegetation index datasets, which can support more reliable trend analysis in global vegetation response to climate changes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Corrosion Behavior of Alloy Structural Steels in Catechol and Biomass-Derived Pyrolysis Oils

Electrochemical impedance spectroscopy (EIS) was used to assess the corrosion behavior of three steels, 2.25Cr-1Mo, 5Cr-1Mo and 9Cr-1Mo (UNS K21590, S50200 and K90941) in bio-oils and catechol, an organic constituent commonly found in bio-oils. Two types of biomass pyrolysis oils produced from selected forest residue feedstocks, classified as high-ash and high-moisture (HAHM) and low-ash and low-moisture (LALM) types and 10 wt.% catechol, were used to test the alloys. According to previous results, 10 wt.% catechol in a water and methanol mixture was corrosive to 2.25Cr-1Mo but not to 9Cr-1Mo, highlighting that increasing Cr content in the steels resulted in improved corrosion resistance. 5Cr-1Mo, a steel with an intermediate Cr content, was tested in catechol solutions to better estimate the critical value of Cr for corrosion resistance under these conditions. Also, the three alloys were tested in both HAHM and LALM bio-oils to assess corrosion susceptibility. For EIS measurements, a customized electrochemical setup was employed to minimize the ohmic path through the bio-oils. The corrosion susceptibility was evaluated by determining the resistance at the alloy-corrosive medium interface.

Jun, Jiheon↗
NASA NTRS1999

Numerical Modeling of Crystal of ZnSe by Physical Vapor Transport - Towards a more Comprehensive Formulations

Crystal growth from the vapor phase has various advantages over melt growth. The main advantage is from a lower processing temperature which makes the process more amenable in instances where the melting temperature of the crystal is high. Other benefits stem from the inherent purification mechanism in the process due to differences in the vapor pressures of the native elements and impurities, and the enhanced interfacial morphological stability during the growth process. Further, the implementation of PVT growth in closed ampoules affords experimental simplicity with minimal needs for complex process control which makes it an ideal candidate for space investigations in systems where gravity tends to have undesirable effects on the growth process. Bulk growth of wide band gap II-VI semiconductors by physical vapor transport has been developed and refined over the past several years at NASA MSFC. Results from a modeling study of PVT crystal growth of ZnSe are reported in this paper. The PVT process is numerically investigated using both two-dimensional and fully three-dimensional formulation of the governing equations and associated boundary conditions. Both the incompressible Boussinesq approximation and the compressible model are tested to determine the influence of gravity on the process and to discern the differences between the two approaches. The influence of a residual gas is included in the models. The results show that both the incompressible and compressible approximations provide comparable results and the presence of a residual gas tends to measurably reduce the mass flux in the system. Detailed flow, thermal and concentration profiles will be provided in the final manuscript along with computed heat and mass transfer rates. Comparisons with the 1-D model will also be provided. The effect of gravity on the process from numerical computations shows subtle effects although experimental evidence from vertically and horizontally grown samples show dramatic evidence of gravitational effects. The shortcomings of the problem formulation will be discussed and a framework will be provided leading up towards a more comprehensive model of PVT systems.

Ramachandran, N.↗
NASA NTRS1998

Crystal Growth of ZnSe by Physical Vapor Transport: A Modeling Study

Crystal growth from the vapor phase has various advantages over melt growth. The main advantage is from a lower processing temperature which makes the process more amenable in instances where the melting temperature of the crystal is high. Other benefits stem from the inherent purification mechanism in the process due to differences in the vapor pressures of the native elements and impurities, and the enhanced interfacial morphological stability during the growth process. Further, the implementation of Physical Vapor Transport (PVT) growth in closed ampoules affords experimental simplicity with minimal needs for complex process control which makes it an ideal candidate for space investigations in systems where gravity tends to have undesirable effects on the growth process. Bulk growth of wide band gap II-VI semiconductors by physical vapor transport has been developed and refined over the past several years at NASA MSFC. Results from a modeling study of PVT crystal growth of ZnSe arc reported in this paper. The PVI process is numerically investigated using both two-dimensional and fully three-dimensional formulation of the governing equations and associated boundary conditions. Both the incompressible Boussinesq approximation and the compressible model are tested to determine the influence of gravity on the process and to discern the differences between the two approaches. The influence of a residual gas is included in the models. The preliminary results show that both the incompressible and compressible approximations provide comparable results and the presence of a residual gas tends to measurably reduce the mass flux in the system. Detailed flow, thermal and concentration profiles will be provided in the final manuscript along with computed heat and mass transfer rates. Comparisons with the 1-D model will also be provided.

Ramachandran, Narayanan↗