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At least 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

The nature of nano-sized precipitates in ferritic/martensitic steel P92 produced by thermomechanical treatment

Thermomechanical treatment (TMT) can effectively improve the mechanical properties of high-Cr ferritic/martensitic (F/M) steels, which has been mainly attributed to a dense dispersion of nano-sized precipitates. Precipitate phases in high-Cr F/M steels produced by TMT require further investigations. Precipitates in commercial F/M steel P92 produced by a TMT process, warm-rolled at 650 °C plus tempered at 650 °C for 1 h, were investigated by transmission electron microscopy. Nano-sized precipitates with a high number density in the steel after the TMT were found to be Cr-rich M{sub 2}(C,N) carbonitride, rather than MX or M{sub 23}C{sub 6} phase. The M{sub 2}(C,N) carbonitride has a hexagonal lattice with the lattice parameters about a/c = 0.299/0.463 nm. These M{sub 2}(C,N) carbonitrides with a typical composition of (Cr{sub 0.85}V{sub 0.06}Fe{sub 0.06}Mo{sub 0.03}){sub 2}(C,N) have an average diameter smaller than 30 nm, and mainly distribute on dislocations and at the boundaries of equiaxed ferrite grains in the TMT steel. The TMT process inhibits the precipitation of M{sub 23}C{sub 6} and M{sub 5}C{sub 2} phases. Enhanced creep properties of the P92 steel after the TMT, as reported previously, were considered to be mainly attributed to plenty of nano-sized Cr-rich M{sub 2}(C,N) carbonitrides produced by the TMT rather than to MX and M{sub 23}C{sub 6} precipitates. - Graphical abstract: TEM micrographs of precipitates on extraction carbon replicas prepared from ferritic/martensitic (F/M) steel P92. (a) After conventional heat treatment, normalized at 1050 °C for 30 min plus tempered at 765 °C for 1 h. (b) After a thermomechanical treatment (TMT), warm-rolled at 650 °C plus tempered at 650 °C for 1 h. Nano-sized precipitates with a high number density in the steel produced by the TMT were found to be Cr-rich M{sub 2}(C,N) carbonitride, rather than MX or M{sub 23}C{sub 6} phase. The TMT process inhibits the precipitation of M{sub 23}C{sub 6} and M{sub 5}C{sub 2} phases. Enhanced creep properties of the steel by the TMT are mainly attributed to plenty of nano-sized Cr-rich M{sub 2}(C,N) carbonitrides, rather than to MX and M{sub 23}C{sub 6} precipitates. It is suggested that plenty of nano-sized Cr-rich M{sub 2}(C,N) carbonitrides produced by the TMT are more helpful to improve the creep properties of the steel than M{sub 23}C{sub 6} plus MX precipitates obtained by the conventional heat treatment. - Highlights: •Carbon replicas were used for precisely identifying nano-sized phases in steel produced by TMT. •Densely nano-sized precipitates in P92 steel after TMT were found to be Cr-rich M{sub 2}(C,N) phase. •Fine precipitates produced by TMT can be M{sub 2}X phase instead of MX and/or M{sub 23}C{sub 6} phase. •Enhanced creep properties of P92 steel by TMT is attributed to fine M{sub 2}X, rather than to MX and M{sub 23}C{sub 6}. •TMT involving a tempering at 650 °C inhibits the formation of M{sub 23}C{sub 6}/M{sub 5}C{sub 2} phases in high-Cr steels.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Coarsening of nanoscale (Ti,Mo)C precipitates in different ferritic matrixes

Coarsening of nanoscale (Ti,Mo)C precipitates within quasi-polygonal ferritic (QPF) matrix and polygonal ferritic (PF) matrix is studied via HRTEM and In-situ heating TEM. Influenced by different nucleation processes, inverse coarsening process showing dissolution of large clustered precipitates and growth of small interphase precipitates is directly observed in the PF matrix. However, due to the evolution of high-density subboundaries, coarsening of precipitates in QPF matrix is mainly characterized by the subboundary migration assisted coarsening process, during which arch-like coarsened precipitates form after the sweeping of subboundaries. Analysis of atomic structure clarify that “imposts” of the arch-like coarsened precipitates are established by the re-precipitation of solute atoms from movable subboundary to the close packed planes {111} of (Ti,Mo)C precipitates, and that the “key stones” are constructed due to the change of re-precipitation planes to {200}. Geometric phase analysis (GPA) illustrates that not only the change of re-precipitation direction during the formation of arch-like precipitates, but also the selective coarsening of clustered precipitates in PF matrix, is caused by the highly anisotropic strain field around (Ti,Mo)C precipitates.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Improving Precipitation Estimation Using Convolutional Neural Network

Precipitation process is generally considered to be poorly represented in numerical weather/climate models. Statistical downscaling (SD) methods, which relate precipitation with model resolved dynamics, often provide more accurate precipitation estimates compared to model's raw precipitation products. Herein we introduce the convolutional neural network model to foster this aspect of SD for daily precipitation prediction. Specifically, we restrict the predictors to the variables that are directly resolved by discretizing the atmospheric dynamics equations. In this sense, our model works as an alternative to the existing precipitation-related parameterization schemes for numerical precipitation estimation. We train the model to learn precipitation-related dynamical features from the surrounding dynamical fields by optimizing a hierarchical set of spatial convolution kernels. We test the model at 14 geogrid points across the contiguous United States. Results show that provided with enough data, precipitation estimates from the convolutional neural network model outperform the reanalysis precipitation products, as well as SD products using linear regression, nearest neighbor, random forest, or fully connected deep neural network. Evaluation for the test set suggests that the improvements can be seamlessly transferred to numerical weather modeling for improving precipitation prediction. Based on the default network, we examine the impact of the network architectures on model performance. Also, we offer simple visualization and analyzing approaches to interpret the models and their results. Our study contributes to the following two aspects: First, we offer a novel approach to enhance numerical precipitation estimation; second, the proposed model provides important implications for improving precipitation-related parameterization schemes using a data-driven approach.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Reconciling Precipitation with Runoff: Observed Hydrological Change in the Midlatitudes

Century-long observed gridded land precipitation datasets are a cornerstone of hydrometeorological research. But recent work has suggested that observed Northern Hemisphere midlatitude (NHML) land mean precipitation does not show evidence of an expected negative response to mid-twentieth-century aerosol forcing. Utilizing observed river discharges, the observed runoff is calculated and compared with observed land precipitation. The results show a near-zero twentieth-century trend in observed NHML land mean runoff, in contrast to the significant positive trend in observed NHML land mean precipitation. However, precipitation and runoff share common interannual and decadal variability. An obvious split, or breakpoint, is found in the NHML land mean runoff-precipitation relationship in the 1930s. Using runoff simulated by six land surface models (LSMs), which are driven by the observed precipitation dataset, such breakpoints are absent. These findings support previous hypotheses that inhomogeneities exist in the early-twentieth-century NHML land mean precipitation record. Adjusting the observed precipitation record according to the observed runoff record largely accounts for the departure of the observed precipitation response from that predicted given the real-world aerosol forcing estimate, more than halving the discrepancy from about 6 to around 2 W m -2 . Consideration of complementary observed runoff adds support to the suggestion that NHML-wide early-twentieth-century precipitation observations are unsuitable for climate change studies. The agreement between precipitation and runoff over Europe, however, is excellent, supporting the use of whole-twentieth-century observed precipitation datasets here.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Improving Monsoon Precipitation Prediction Using Combined Convolutional and Long Short Term Memory Neural Network

Precipitation downscaling is widely employed for enhancing the resolution and accuracy of precipitation products from general circulation models (GCMs). In this study, we propose a novel statistical downscaling method to foster GCMs’ precipitation prediction resolution and accuracy for the monsoon region. We develop a deep neural network composed of a convolution and Long Short Term Memory (LSTM) recurrent module to estimate precipitation based on well-resolved atmospheric dynamical fields. The proposed model is compared against the GCM precipitation product and classical downscaling methods in the Xiangjiang River Basin in South China. Results show considerable improvement compared to the European Centre for Medium-Range Weather Forecasts (ECMWF)-Interim reanalysis precipitation. Also, the model outperforms benchmark downscaling approaches, including (1) quantile mapping, (2) the support vector machine, and (3) the convolutional neural network. To test the robustness of the model and its applicability in practical forecasting, we apply the trained network for precipitation prediction forced by retrospective forecasts from the ECMWF model. Compared to the ECMWF precipitation forecast, our model makes better use of the resolved dynamical field for more accurate precipitation prediction at lead times from 1 day up to 2 weeks. This superiority decreases with the forecast lead time, as the GCM’s skill in predicting atmospheric dynamics is diminished by the chaotic effect. Finally, we build a distributed hydrological model and force it with different sources of precipitation inputs. Hydrological simulation forced with the neural network precipitation estimation shows significant advantage over simulation forced with the original ERA-Interim precipitation (with NSE value increases from 0.06 to 0.64), and the performance is only slightly worse than the observed precipitation forced simulation (NSE = 0.82). This further proves the value of the proposed downscaling method, and suggests its potential for hydrological forecasts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

A new ionospheric electron precipitation module coupled with RAM-SCB within the geospace general circulation model

Electron precipitation down to the atmosphere due to wave-particle scattering in the magnetosphere contributes significantly to the auroral ionospheric conductivity. In order to obtain the auroral conductivity in global MHD models that are incapable of capturing kinetic physics in the magnetosphere, MHD parameters are often used to estimate electron precipitation flux for the conductivity calculation. Such an MHD approach, however, lacks self-consistency in representing the magnetosphere-ionosphere coupling processes. In this study we improve the coupling processes in global models with a more physical method. We calculate the physics-based electron precipitation from the ring current and map it to the ionospheric altitude for solving the ionospheric electrodynamics. In particular, we use the BATS-R-US (Block Adaptive Tree Scheme-Roe type-Upstream) MHD model coupled with the kinetic ring current model RAM-SCB (Ring current-Atmosphere interaction Model with Self-Consistent Magnetic field (B)) that solves pitch angle-dependent electron distribution functions, to study the global circulation dynamics during the 25–26 January 2013 storm event. Since the electron precipitation loss is mostly governed by wave-particle resonant scattering in the magnetosphere, we further investigate two loss methods of specifying electron precipitation loss associated with wave-particle interactions: (1) using pitch angle diffusion coefficients D αα (E,α) determined from the quasi-linear theory, with wave spectral and plasma density obtained from statistical observations (named as “diffusion coefficient method”) and (2) using electron lifetimes τ(E) independent on pitch angles inferred from the above diffusion coefficients (named as “lifetime method”). We found that both loss methods demonstrate similar temporal evolution of the trapped ring current electrons, indicating that the impact of using different kinds of loss rates is small on the trapped electron population. Furthermore, for the precipitated electrons, the lifetime method hardly captures any precipitation in the large L shell (i.e., 4 < L < 6.5) region, while the diffusion coefficient method produces much better agreement with NOAA/POES measurements, including the spatial distribution and temporal evolution of electron precipitation in the region from the premidnight through the dawn to the dayside. Further comparisons of the precipitation energy flux to DMSP observations indicates that the new physics-based precipitation approach using diffusion coefficients for the ring current electron loss can explain the diffuse electron precipitation in the dawn sector, such as the enhanced precipitation flux at auroral latitudes and flux drop near the subauroral latitudes, but the traditional MHD approach largely overestimates the precipitation flux at lower latitudes.

58 GEOSCIENCES↗
DOE OSTI2020

Crystallographic orientation-dependent strain hardening in a precipitation-strengthened Al-Cu alloy

While the strengthening of Al-Cu alloys due to precipitation has been extensively studied, the effect of crystallographic orientation of the matrix and precipitates, as well as precipitate morphology, on the strain hardening behavior is not well understood. In this paper, we investigate this effect with in situ neutron diffraction during deformation of an Al-Cu alloy (206) after multiple aging treatments. Precipitate-dislocation interactions were found to change from precipitate shearing for microstructures predominantly containing GPI and θ" precipitates to Orowan looping for microstructures with primarily θ' and θ precipitates. Notably, significant anisotropy in strain hardening behavior was observed when θ' precipitates were present, which was attributed to crystallographic orientation dependent load transfer from the Al matrix to the θ' precipitates. The anisotropic load transfer is hypothesized to be caused by the extent of rotation of high aspect-ratio θ' precipitates, owing to dislocations looping around them during plastic deformation of the matrix. Predictions from an analytical model describing the anisotropic magnitude of load transfer from precipitate rotation agree well with experimental results, successfully validating the precipitate rotation hypothesis and explaining the anisotropic strain hardening behavior. This model allows for the prediction of stresses separately in the precipitate and matrix phases as a function of crystallographic orientation, only given the bulk mechanical properties.

36 MATERIALS SCIENCE↗
DOE OSTI2021

How much information on precipitation is contained in satellite infrared imagery?

Satellite infrared (IR) brightness temperature providing information on the characteristics of cloud tops is one of the primary input data used for estimating precipitation from satellites. Thus, the natural questions to ask are: Does IR imagery contain more information about precipitation in some regions more than others? What are the factors that lead to such differences? Should we expect the amount of information provided by IR imagery to change with respect to temporal or spatial aggregation? Comprehensive analysis of the accuracy of satellite-based precipitation estimates obtained from any given algorithm may provide qualitative answers to the above questions. However, such an approach only offers conclusions within the limited context of the algorithm used to obtain precipitation estimates; that is, patterns caused by the inherent information content of IR imagery are inextricable from those induced by assumptions embedded in the precipitation estimation algorithm. Here, we address the abovementioned questions from a different perspective using an information-theoretic measure, free of structural assumptions and general to a wide range of relationships, to characterize the average and seasonal dependence between IR imagery provided by satellite sensors in the spectral range (10.7–11.5 μm) and precipitation rates in the contiguous United States (CONUS) across distinct temporal and spatial scales. We analyze a total of more than 1.3 billion pairs of IR and precipitation observations over CONUS, and we observe interesting patterns. First, we show that there is a strong inverse relationship (ρ = - 0.73) between the information content of IR data and the number of no-rain observations; however, its robustness varies regionally and seasonally with less significant correlation during the warm season. Second, we demonstrate that the intuitive relationship of increasing dependence between IR and precipitation as a result of temporal or spatial aggregation exhibits a diminishing returns behavior. For instance, temporal aggregation from 1 to 3 h increases the dependence approximately 7 times as much as temporal aggregation from 12 to 24 h. Furthermore, we quantitatively examine and re-confirm several statements previously reported in the literature such as the strong association between IR and precipitation in convective storms and the low association in orographic rainfall. Finally, we conclude by illustrating the potential of the analysis in diagnosis of operational algorithms for estimating precipitation from IR brightness temperature. The findings of this study pinpoint spatial domains and time scales at which IR is not an adequate proxy for estimating precipitation. Thus, they can potentially guide the development of operational algorithms that utilize satellite Infrared imagery for estimating, downscaling and data fusion of precipitation. Finally, the analysis presented here opens up the possibility of developing new methodologies for diagnosis of satellite-based precipitation estimation algorithms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Characteristics of Ice Cloud–Precipitation of Warm Season Mesoscale Convective Systems over the Great Plains

Abstract In this study, the mesoscale convective systems (MCSs) are tracked using high-resolution radar and satellite observations over the U.S. Great Plains during April–August from 2010 to 2012. The spatiotemporal variability of MCS precipitation is then characterized using the Stage IV product. We found that the spatial variability and nocturnal peaks of MCS precipitation are primarily driven by the MCS occurrence rather than the precipitation intensity. The tracked MCSs are further classified into convective core (CC), stratiform rain (SR), and anvil clouds regions. The spatial variability and diurnal cycle of precipitation in the SR regions of MCSs are not as significant as those of MCS precipitation. In the SR regions, the high-resolution, long-term ice cloud microphysical properties [ice water content (IWC) and ice water paths (IWPs)] are provided. The IWCs generally decrease with height. Spatially, the IWC, IWP, and precipitation are all higher over the southern Great Plains than over the northern Great Plains. Seasonally, those ice and precipitation properties are all higher in summer than in spring. Comparing the peak timings of MCS precipitation and IWPs from the diurnal cycles and their composite evolutions, it is found that when using the peak timing of IWPSR as a reference, the heaviest precipitation in the MCS convective core occurs earlier, while the strongest SR precipitation occurs later. The shift of peak timings could be explained by the stratiform precipitation formation process. The IWP and precipitation relationships are different at MCS genesis, mature, and decay stages. The relationships and the transition processes from ice particles to precipitation also depend on the low-level humidity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Evaluating 3 decades of precipitation in the Upper Colorado River basin from a high-resolution regional climate model

Abstract. Convection-permitting regional climate models (RCMs) have recently become tractable for applications at multi-decadal timescales. These types of models have tremendous utility for water resource studies, but better characterization of precipitation biases is needed, particularly for water-resource-critical mountain regions, where precipitation is highly variable in space, observations are sparse, and the societal water need is great. This study examines 34 years (1987–2020) of RCM precipitation from the Weather Research and Forecasting model (WRF; v3.8.1), using the Climate Forecast System Reanalysis (CFS; CFSv2) initial and lateral boundary conditions and a 1 km × 1 km innermost grid spacing. The RCM is centered over the Upper Colorado River basin, with a focus on the high-elevation, 750 km2 East River watershed (ERW), where a variety of high-impact scientific activities are currently ongoing. Precipitation is compared against point observations (Natural Resources Conservation Service Snow Telemetry or SNOTEL), gridded climate datasets (Newman, Livneh, and PRISM), and Bayesian reconstructions of watershed mean precipitation conditioned on streamflow and high-resolution snow remote-sensing products. We find that the cool-season precipitation percent error between WRF and 23 SNOTEL gauges has a low overall bias (x^ = 0.25 %, s = 13.63 %) and that WRF has a higher percent error during the warm season (x^ = 10.37 %, s = 12.79 %). Warm-season bias manifests as a high number of low-precipitation days, though the low-resolution or SNOTEL gauges limit some of the conclusions that can be drawn. Regional comparisons between WRF precipitation accumulation and three different gridded datasets show differences on the order of ± 20 %, particularly at the highest elevations and in keeping with findings from other studies. We find that WRF agrees slightly better with the Bayesian reconstruction of precipitation in the ERW compared to the gridded precipitation datasets, particularly when changing SNOTEL densities are taken into account. The conclusions are that the RCM reasonably captures orographic precipitation in this region and demonstrates that leveraging additional hydrologic information (streamflow and snow remote-sensing data) improves the ability to characterize biases in RCM precipitation fields. Error characteristics reported in this study are essential for leveraging the RCM model outputs for studies of past and future climates and water resource applications. The methods developed in this study can be applied to other watersheds and model configurations. Hourly 1 km × 1 km precipitation and other meteorological outputs from this dataset are publicly available and suitable for a wide variety of applications.

Geology↗
DOE OSTI2023

Implicit learning of convective organization explains precipitation stochasticity

Accurate prediction of precipitation intensity is crucial for both human and natural systems, especially in a warming climate more prone to extreme precipitation. Yet, climate models fail to accurately predict precipitation intensity, particularly extremes. One missing piece of information in traditional climate model parameterizations is subgrid-scale cloud structure and organization, which affects precipitation intensity and stochasticity at coarse resolution. Here, using global storm-resolving simulations and machine learning, we show that, by implicitly learning subgrid organization, we can accurately predict precipitation variability and stochasticity with a low-dimensional set of latent variables. Using a neural network to parameterize coarse-grained precipitation, we find that the overall behavior of precipitation is reasonably predictable using large-scale quantities only; however, the neural network cannot predict the variability of precipitation (R 2 ~ 0.45) and underestimates precipitation extremes. The performance is significantly improved when the network is informed by our organization metric, correctly predicting precipitation extremes and spatial variability (R 2 ~ 0.9). The organization metric is implicitly learned by training the algorithm on a high-resolution precipitable water field, encoding the degree of subgrid organization. The organization metric shows large hysteresis, emphasizing the role of memory created by subgrid-scale structures. We demonstrate that this organization metric can be predicted as a simple memory process from information available at the previous time steps. These findings stress the role of organization and memory in accurate prediction of precipitation intensity and extremes and the necessity of parameterizing subgrid-scale convective organization in climate models to better project future changes of water cycle and extremes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Energetic Electron Precipitation Driven by Electromagnetic Ion Cyclotron Waves from ELFIN’s Low Altitude Perspective

We review comprehensive observations of electromagnetic ion cyclotron (EMIC) wave-driven energetic electron precipitation using data collected by the energetic electron detector on the Electron Losses and Fields InvestigatioN (ELFIN) mission, two polar-orbiting low-altitude spinning CubeSats, measuring 50-5000 keV electrons with good pitch-angle and energy resolution. EMIC wave-driven precipitation exhibits a distinct signature in energy-spectrograms of the precipitating-to-trapped flux ratio: peaks at >0.5 MeV which are abrupt (bursty) (lasting ~17 s, or ΔL ~ 0.56) with significant substructure (occasionally down to sub-second timescale). We attribute the bursty nature of the precipitation to the spatial extent and structuredness of the wave field at the equator. Multiple ELFIN passes over the same MLT sector allow us to study the spatial and temporal evolution of the EMIC wave - electron interaction region. Case studies employing conjugate ground-based or equatorial observations of the EMIC waves reveal that the energy of moderate and strong precipitation at ELFIN approximately agrees with theoretical expectations for cyclotron resonant interactions in a cold plasma. Using multiple years of ELFIN data uniformly distributed in local time, we assemble a statistical database of ~50 events of strong EMIC wave-driven precipitation. Most reside at L ~ 5 – 7 at dusk, while a smaller subset exists at L ~ 8 – 12 at post-midnight. The energies of the peak-precipitation ratio and of the half-peak precipitation ratio (our proxy for the minimum resonance energy) exhibit an L-shell dependence in good agreement with theoretical estimates based on prior statistical observations of EMIC wave power spectra. The precipitation ratio’s spectral shape for the most intense events has an exponential falloff away from the peak (i.e., on either side of ~ 1.45 MeV). It too agrees well with quasi-linear diffusion theory based on prior statistics of wave spectra. It should be noted though that this diffusive treatment likely includes effects from nonlinear resonant interactions (especially at high energies) and nonresonant effects from sharp wave packet edges (at low energies). Sub-MeV electron precipitation observed concurrently with strong EMIC wave-driven >1 MeV precipitation has a spectral shape that is consistent with efficient pitch-angle scattering down to ~ 200-300 keV by much less intense higher frequency EMIC waves at dusk (where such waves are most frequent). At ~100 keV, whistler-mode chorus may be implicated in concurrent precipitation. These results confirm the critical role of EMIC waves in driving relativistic electron losses. Nonlinear effects may abound and require further investigation.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Precipitation of calcium carbonate and calcium phosphate under diffusion controlled mixing

Multi-component mineral precipitation in porous, subsurface environments is challenging to simulate or engineer when in situ reactant mixing is controlled by diffusion. In contrast to well-mixed systems, the conditions that favor mineral precipitation in porous media are distributed along chemical gradients, which evolve spatially due to concurrent mineral precipitation and modification of solute transport in the media. The resulting physical and chemical characteristics of a mixing/precipitation zone are a consequence of coupling between transport and chemical processes, and the distinctive properties of individual chemical systems. We examined the spatial distribution of precipitates formed in “double diffusion” columns for two chemical systems, calcium carbonate and calcium phosphate. Polyacrylamide hydrogel was used as a low permeability, high porosity medium to maximize diffusive mixing and minimize pressure- and density-driven flow between reactant solutions. In the calcium phosphate system, multiple, visually dense and narrow bands of precipitates were observed that were reminiscent of previously reported Liesegang patterns. In the calcium carbonate system, wider precipitation zones characterized by more sparse distributions of precipitates and a more open channel structure were observed. In both cases, formation of precipitates inhibited, but did not necessarily eliminate, continued transport and mixing of the reactants. A reactive transport model with fully implicit coupling between diffusion, chemical speciation and precipitation kinetics, but where explicit details of nucleation processes were neglected, was able to qualitatively simulate properties of the precipitation zones. The results help to illustrate how changes in the physical properties of a precipitation zone depend on coupling between diffusion-controlled reactant mixing and chemistry-specific details of precipitation kinetics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

Effect of solution annealing temperature on precipitation in 2205 duplex stainless steel

In the present study, effect of solution annealing temperature (1050 Degree-Sign C and 1100 Degree-Sign C) and isothermal ageing (700 Degree-Sign C: 15 min to 6 h) on the microstructural changes in 2205 duplex stainless steel has been investigated systematically. Scanning electron microscopy and X-ray diffraction were adopted to follow the microstructural evolution, while an energy dispersive spectrometer attached to scanning electron microscope was used to obtain localised chemical information of various phases. The ferritic matrix of the two phase 2205 duplex stainless steel ({approx} 45% ferrite and {approx} 55% austenite) undergoes a series of metallurgical transformations during ageing-formation of secondary austenite ({gamma}{sub 2}) and precipitation of Cr and Mo rich intermetallic (chi-{chi} and sigma-{sigma}) phases. For solution annealing at 1050 Degree-Sign C, significant amount of carbides were observed in the ferrite grains after 1 h of ageing at 700 Degree-Sign C. {chi} Phase precipitated after the precipitation of carbides-preferentially at the ferrite-ferrite and also at the ferrite-austenite boundaries. {sigma} Phase was not observed in significant quantity even after 6 h of ageing. The sequence of precipitation in samples solution annealed at 1050 Degree-Sign C was found to be carbides {yields} {chi} {yields} {sigma}. On the contrary, for samples solution annealed at 1100 Degree-Sign C, the precipitation of {chi} phase was negligible. {chi} Phase precipitated before {sigma} phase, preferentially along the ferrite-ferrite grain boundaries and was later consumed in the {sigma} phase precipitation. The {sigma} phase precipitated via the eutectoid transformation of ferrite to yield secondary austenite {gamma}{sub 2} and {sigma} phase in the ferrite and along the ferrite-austenite grain boundaries. An increase in the volume fraction of {gamma}{sub 2} and {sigma} phase with simultaneous decrease in the ferrite was evidenced with ageing. - Highlights: Black-Right-Pointing-Pointer Effect of solution annealing temperature on microstructural evolution is studied. Black-Right-Pointing-Pointer {chi} Phase precipitated preferentially in the samples solution annealed at 1050 Degree-Sign C. Black-Right-Pointing-Pointer {sigma} Phase precipitated preferentially in the samples solution annealed at 1100 Degree-Sign C. Black-Right-Pointing-Pointer For SA at 1050 Degree-Sign C, the sequence of precipitation was carbides {yields} {chi} phase {yields} {sigma} phase. Black-Right-Pointing-Pointer For SA at 1100 Degree-Sign C, {sigma} phase precipitated via the eutectoid reaction: ferrite (F) {yields} {sigma} + {gamma}{sub 2}.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Characterisation of Laves phase precipitation and its correlation to creep rupture strength of ferritic steels

The Laves phase precipitation process was characterised by means of field emission scanning electron microscopy to demonstrate its effect on creep rupture strength of steels with a fully ferritic matrix. To eliminate the effects of carbide and carbonitride precipitations so that the creep rupture data can be analysed exclusively in relation to the Laves phase precipitation process, an alloy Fe–9Cr–3Co–3W (wt.%) without C and N additions was used for the study. Creep rupture strengths were measured and volume fraction and particle size of Laves phase precipitates in the ruptured specimens were analysed. It was found that the creep rupture strength started to collapse (or decrease more rapidly) long before the Laves phase precipitation reached equilibrium fraction. This was related to the onset of the coarsening of Laves phase particles, which precipitated only on grain boundaries and hence contributed little to precipitation strengthening. Creep deformation had no effect either on the precipitation kinetics or on the growth kinetics of Laves phase particles. - Highlights: • Laves phase precipitation at 650 °C was characterised for Fe–9Cr–3W–3Co alloy. • Laves phase precipitated predominantly on grain boundaries. • Creep deformation had no effect on Laves phase precipitation and growth kinetics. • Creep strength started to collapse long before Laves phase precipitation is ended. • Collapse of creep strength was attributed to the coarsening of Laves phase particles.

36 MATERIALS SCIENCE↗
DOE OSTI2018

The evolution of precipitate crystal structures in an Al-Mg-Si(-Cu) alloy studied by a combined HAADF-STEM and SPED approach

Highlights: • Scanning electron diffraction and machine learning complement lattice imaging • Developed approach gives increased objectivity in precipitate phase analysis • Precipitate phase fractions and solute content obtained with superior statistics • 0.01 at.% Cu changes the Al-Mg-Si system precipitation during overageing • Q’-phase eventually dominates precipitate structures and shows varying Cu-occupancy - Abstract: This work presents a detailed investigation into the effect of a low Cu addition (0.01 at.%) on precipitation in an Al-0.80Mg-0.85Si alloy during ageing. The precipitate crystal structures were assessed by scanning transmission electron microscopy combined with a novel scanning precession electron diffraction approach, which includes machine learning. The combination of techniques enabled evaluation of the atomic arrangement within individual precipitates, as well as an improved estimate of precipitate phase fractions at each ageing condition, through analysis of a statistically significant number of precipitates. Based on the obtained results, the total amount of solute atoms locked inside precipitates could be approximated. It was shown that even with a Cu content close to impurity levels, the Al-Mg-Si system precipitation was significantly affected with overageing. The principal change was due to a gradually increasing phase fraction of the Cu-containing Q′-phase, which eventually was seen to dominate the precipitate structures. The structural overtake could be explained based on a continuous formation of the thermally stable Q′-phase, with Cu atomic columns incorporating less Cu than what could potentially be accommodated.

36 MATERIALS SCIENCE↗
DOE OSTI2012

Synthesis of high surface area ZnO powder by continuous precipitation

Graphical abstract: High surface area ZnO powders are synthesized by a low temperature continuous precipitation under ultrasonication. Urea is used as precipitating agent so that no contamination of ZnO powder emanating from precipitating agent, such as, alkalis, is observed. pH and type of precursor greatly affects the surface area and other properties. In this manuscript, we report a very simple and effective continuous precipitation to synthesize high surface area ZnO powder. Highlights: Black-Right-Pointing-Pointer The synthesis of high surface area ZnO powder was achieved at 90 Degree-Sign C in a continuous precipitation unit. Black-Right-Pointing-Pointer Continuous precipitation unit was ultrasonicated to improve final product homogeneity. Black-Right-Pointing-Pointer Precipitation intermediate, hydrozincite, was led to high surface area ZnO powder. Black-Right-Pointing-Pointer The synthesized ZnO nanoparticles had a rather uniform mesoporous structure. -- Abstract: Synthesis of high surface area ZnO powder was achieved by continuous precipitation using zinc ions and urea at low temperature of 90 Degree-Sign C. The powder precipitated resulted in high-purity single-phase ZnO powder when calcined at 280 Degree-Sign C for 3 h in air. The solution pH and the precipitation duration strongly affected the surface area of the calcined ZnO powder. Detailed structural characterizations demonstrated that the synthesized ZnO powder were single crystalline with wurtzite hexagonal phase. The powdered samples precipitated by homogeneous precipitation crystallized directly to hydrozincite without any intermediate phase formation. The phase structures, morphologies and properties of the final ZnO powders were investigated by X-ray diffraction (XRD), scanning electron microscopy (SEM), dynamic light scattering particle size analysis (DLS), and nitrogen physisorption in order to determine the specific surface area (BET) and the pore size distribution (BJH).

36 MATERIALS SCIENCE↗
DOE OSTI2021

Understanding the interaction of extension twinning and basal-plate precipitates in Mg-9Al using precession electron diffraction

Precession electron diffraction is used to characterize the interaction between ${10\bar{1}2}$ tensile twins and basal plate-like precipitates in a post-deformed, precipitate-dispersed Mg-9Al micropillar. We observed a heterogeneous distribution of precipitates in the micropillar sample, which enabled the study of the different stages involved in twin precipitate interactions. We show that twin nucleation was promoted, taking place on the surface, as well as from the interior of the micropillar. Twin tip propagation and twin growth were hindered by the precipitates. Twin tips either were arrested by precipitates and new twins formed on the other side of the precipitate, or continued to grow around the precipitate without the re-nucleation events. During twin thickening, the precipitates did not significantly alter the shear-dominant twin boundary migration mechanism, as evidenced by the relatively flat twin boundaries around the partially embedded precipitates. However, the twin boundary migration was retarded by the precipitates, especially in regions confined by closely-spaced precipitates.

36 MATERIALS SCIENCE↗