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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Stepwise allomorphic transformations by alkaline and ethylenediamine treatments on bamboo crystalline cellulose for enhanced enzymatic digestibility

Efficient modification and decrystallization of cellulose Iβ in lignocellulosic biomass are potential ways to overcome biomass recalcitrance and facilitates enzymatic degradation. Herein, we have treated moso bamboo and microcrystalline cellulose separately by either alkali or ethylenediamine (EDA) treatments, as well as using both in succession. The pretreatments successfully transformed the initial cellulose Iβ allomorph into cellulose II, III I , and cellulose III II . Crystal structures and crystallinities were determined by Rietveld X-ray diffraction and CP/MAS 13 C NMR. Comparatively, the samples that were sequentially treated by EDA and NaOH had low crystallinity (~50%), larger d-spacings of ~4.4 Å for the hydrophobic (110) planes, and less condensed microfibrillar aggregation. The factors simultaneously contributed to as high enzymatic hydrolysis yield as 89.5% and 91.7% respectively, followed by the yields of mercerized cellulose II and cellulose III II allomorph. Here, the stepwise allomorphic transformations may provide new strategies on improving efficiency of saccharification process and biomass utilization.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Vapor transport growth of MnBi 2 Te 4 and related compounds

Motivated by fine tuning of the magnetic and topological properties of MnBi 2 Te 4 via defect engineering, in this work, we report the crystal growth of MnBi 2 Te 4 and related compounds using vapor transport method and crystal characterization by measuring elemental ratio, magnetic and transport properties, and scanning tunneling microscopy. For the growth of MnBi 2 Te 4 single crystals, I 2 , MnI 2 , MnCl 2 , TeCl 4 , and MoCl 5 are all effective transport agents; chemical transportation occurs faster in the presence of iodides than chlorides. We further successfully grow MnSb 2 Te 4 , MnBi 2-x Sb x Te 4 , and Sb-doped MnBi 4 Te 7 crystals. A small temperature gradient< 20°C between the hot and cold ends of the growth ampoule is critical for the successful crystal growth of MnBi 2 Te 4 and related compounds. Compared to flux grown crystals, vapor transported crystals tend to be Mn stoichiometric, and Sb-bearing compositions have more Mn/Sb site mixing. The vapor transport growth provides a new materials synthesis approach to fine tune the magnetic and topological properties of these intrinsic magnetic topological insulators where controlling defects is vital.

36 MATERIALS SCIENCE↗
DOE OSTI2020

A review of spatial Markov models for predicting pre-asymptotic and anomalous transport in porous and fractured media

Heterogeneity across a broad range of scales in geologic porous media often manifests in observations of non-Fickian or anomalous transport. While traditional anomalous transport models can successfully make predictions in certain geological systems, increasing evidence suggests that assumptions relating to independent and identically distributed increments constrain where and when they can be reliably applied. A relatively novel model, the Spatial Markov model (SMM), relaxes the assumption of independence. The SMM belongs to the family of correlated continuous time random walks and has shown promise across a wide range of transport problems relevant to natural porous media. It has been successfully used to model conservative as well as more recently reactive transport in highly complex flows ranging from pore scales to much larger scales of interest in geology and subsurface hydrology. In this review paper we summarize its original development and provide a comprehensive review of its advances and applications as well as lay out a vision for its future development.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

On numerical errors to the fields surrounding a relativistically moving particle in PIC codes

The particle-in-cell (PIC) method is widely used to model the self-consistent interaction between discrete particles and electromagnetic fields. It has been successfully applied to problems across plasma physics including plasma based acceleration, inertial confinement fusion, magnetically confined fusion, space physics, astrophysics, high energy density plasmas. In many cases the physics involves how relativistic particles (those with high relativistic γ factors) are generated and interact with plasmas. However, when relativistic particles stream across the grid, both in “vacuum” and in plasma, many numerical issues may arise which can lead to unphysical results. We present a detailed analysis of how discretized Maxwell solvers used in PIC codes can lead to numerical errors to the fields that surround particles that move at relativistic speeds across the grid. Expressions for the axial electric field as integrals in k space are presented that reveal two types of errors. The first arises from errors to the numerator of the integrand and leads to unphysical fields that are antisymmetric about the particle. Furthermore, the second arises from errors to the denominator of the integrand and lead to Cherenkov like radiation in “vacuum”. These fields are not anti-symmetric, extend behind the particle, and cause the particle to accelerate or decelerate depending on the solver and parameters. The unphysical fields are studied in detail for two representative solvers - the Yee solver and the FFT based solver. Although the Cherenkov fields are absent, the space charge fields are still present in the fundamental Brillouin zone for the FFT based solvers. In addition, the Cherenkov fields are present in higher order zones for the FFT based solvers. Comparison between the analytical solutions and PIC simulation results are presented. A solution for eliminating these unphysical fields by modifying the k operator in the axial direction is also presented. Using a customized finite difference solver, this solution was successfully implemented into OSIRIS [1]. Results from the customized solver are also presented. Additionally, this solution will be useful for a beam of particles that all move in one direction with a small angular divergence.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Bayesian sparse learning with preconditioned stochastic gradient MCMC and its applications

Deep neural networks have been successfully employed in an extensive variety of research areas, including solving partial differential equations. Despite its significant success, there are some challenges in effectively training DNN, such as avoiding overfitting in over-parameterized DNNs and accelerating the optimization in DNNs with pathological curvature. Here, we propose a Bayesian type sparse deep learning algorithm. The algorithm utilizes a set of spike-and-slab priors for the parameters in the deep neural network. The hierarchical Bayesian mixture will be trained using an adaptive empirical method. That is, one will alternatively sample from the posterior using preconditioned stochastic gradient Langevin Dynamics (PSGLD), and optimize the latent variables via stochastic approximation. The sparsity of the network is achieved while optimizing the hyperparameters with adaptive searching and penalizing. A popular SG-MCMC approach is Stochastic gradient Langevin dynamics (SGLD). However, considering the complex geometry in the model parameter space in nonconvex learning, updating parameters using a universal step size in each component as in SGLD may cause slow mixing. To address this issue, we apply a computationally manageable preconditioner in the updating rule, which provides a step-size parameter to adapt to local geometric properties. Moreover, by smoothly optimizing the hyperparameter in the preconditioning matrix, our proposed algorithm ensures a decreasing bias, which is introduced by ignoring the correction term in the preconditioned SGLD. According to the existing theoretical framework, we show that the proposed algorithm can asymptotically converge to the correct distribution with a controllable bias under mild conditions. Numerical tests are performed on both synthetic regression problems and learning solutions of elliptic PDE, which demonstrate the accuracy and efficiency of the present work.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Identifying and tracking bubbles and drops in simulations: A toolbox for obtaining sizes, lineages, and breakup and coalescence statistics

Knowledge of bubble and drop size distributions in two-phase flows is important for characterizing a wide range of phenomena, including combustor ignition, sonar communication, and cloud formation. The physical mechanisms driving the background flow also drive the time evolution of these distributions. Accurate and robust identification and tracking algorithms for the dispersed phase are necessary to reliably measure this evolution and thereby quantify the underlying mechanisms in interface-resolving flow simulations. The identification of individual bubbles and drops traditionally relies on an algorithm used to identify connected regions. This traditional algorithm can be sensitive to the presence of spurious structures. A cost-effective refinement is proposed to maximize volume accuracy while minimizing the identification of spurious bubbles and drops. An accurate identification scheme is crucial for distinguishing bubble and drop pairs with large size ratios. The identified bubbles and drops need to be tracked in time to obtain breakup and coalescence statistics that characterize the evolution of the size distribution, including breakup and coalescence frequencies, and the probability distributions of parent and child bubble and drop sizes. An algorithm based on mass conservation is proposed to construct bubble and drop lineages using simulation snapshots that are not necessarily from consecutive time steps. These lineages are then used to detect breakup and coalescence events, and obtain the desired statistics. Accurate identification of large-size-ratio bubble and drop pairs enables accurate detection of breakup and coalescence events over a large size range. Accurate detection of successive breakup and coalescence events requires that the snapshot interval be an order of magnitude smaller than the characteristic breakup and coalescence times to capture these successive events while minimizing the identification of repeated confounding events. Together, these algorithms serve as a toolbox for detailed analysis of two-phase simulations, and enable insights into the mechanisms behind bubble and drop formation and evolution in flows of practical importance.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Influence of Pt-Metal Alloy Catalysts with Various Ionomers on Oxygen Reduction Reaction in Fuel Cell Application

Pt-M/C (M = Co, Ni, Mn, etc.) alloy catalysts exhibit superior oxygen reduction reaction (ORR) activity compared to pure Pt/C, leading to a high energy efficiency in hydrogen fuel cells. However, many Pt-M/C alloy catalysts were synthesized and evaluated at the lab scale in model test-bed systems like rotating disc electrodes, which don't always correlate to performance within a fuel cell system; there is a clear need to evaluate catalysts in electrodes that can be prepared at industrially relevant scales to evaluate how factors like ink formulation can greatly affect device-level of fuel cell performance. Herein, three commercial Pt-M/C alloy catalysts (two Pt-Co/C and one Pt-Ni/C) were comprehensively characterized by various techniques. The results show that the average particle sizes of the three catalysts are close to 5 nm; the atomic ratio of Pt/M is around 4; and the M was successfully embedded into Pt lattice, resulting in the positive shift of Pt 4f in XPS spectra and XRD patterns. These catalytic materials were incorporated into 9 different cathode catalyst layers (CCLs) with three kinds of ionomers (Nafion D2020, high oxygen permeability ionomer (HOPI), and Aquivion D79-25BS), and their performance in proton exchange membrane fuel cells (PEMFCs) were investigated. The results demonstrate that the Pt-Co/C catalysts possess a higher mass activity (MA) than Pt-Ni/C; the cathodes with Nafion ionomer provide the highest MA while electrodes with Aquivion ionomer showed the lowest activity, attributed to poor H+ conductivity resulting from suboptimal ionomer incorporation. Finally, these alloys were shown to exceed DOE targets for MA and H2/Air performance reported in the recent publications at beginning of life and after 90k cycle catalyst AST protocol. This study provides valuable performance benchmarks for these materials guiding future Pt-M/C catalyst design and material integration for heavy duty PEMFC applications.

08 HYDROGEN↗
DOE OSTI2021

Critical review of functionalized silica sorbent strategies for selective extraction of rare earth elements from acid mine drainage

We report the ubiquitous and growing global reliance on rare earth elements (REEs) for modern technology and the need for reliable domestic sources underscore the rising trend in REE-related research. Adsorption-based methods for REE recovery from liquid waste sources are well-positioned to compete with those of solvent extraction, both because of their expected lower negative environmental impact and simpler process operations. Functionalized silica represents a rising category of low cost and stable sorbents for heavy metal and REE recovery. These materials have collectively achieved high capacity and/or high selective removal of REEs from ideal solutions and synthetic or real coal wastewater and other leachate source. These sorbents are competitive with conventional materials, such as ion exchange resins, activated carbon; and novel polymeric materials like ion-imprinted particles and metal organic frameworks (MOFs). This critical review first presents a data mining analysis for rare earth element recovery publications indexed in Web of science, highlighting changes in REE recovery research foci and confirming the sharply growing interest in functionalized silica sorbents. A detailed examination of sorbent formulation and operation strategies to selectively separate heavy (HREE), middle (MREE), and light (LREE) REEs from the aqueous sources is presented. Selectivity values for sorbents were largely calculated from available figure data and gauged the success of the associated strategies, primarily: (1) silane-grafted ligands, (2) impregnated ligands, and (3) bottom-up ligand/silica hybrids. These were often accompanied by successful co-strategies, especially bite angle control, site saturation, and selective REE elution. Recognizing the need to remove competing fouling metals to achieve purified REE “baskets,” we highlight techniques for eliminating these species from acid mine drainage (AMD) and suggest a novel adsorption-based process for purified REE extraction that could be adapted to different water systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Copper carbon composite wire with a uniform carbon dispersion made by friction extrusion

Copper-carbon composites are a group of materials with excellent mechanical, electrical, thermal, and tribological properties. However, bulk size copper-carbon composites made by the traditional manufacturing processes, like rolling or extrusion, fall short of reaching some of these properties predicted by theory or demonstrated only by samples at centimeter scale or smaller. The two main challenges to the successful scaling-up are: 1) to uniformly disperse carbon in the metal matrix; 2) to avoid degradation due to oxidation or reaction from overheating. In this work, we first demonstrate friction extrusion as a new method to make bulk-size void-free copper-carbon composite wires with homogenized carbon dispersion. Three different carbon varieties, graphite powder, graphene nanopowder, and carbon nanotubes, were added to the copper matrix with the concentration ranging from 0.5 wt% to 15 wt%. Special tooling, processing parameters, and procedures were developed, especially for high carbon content samples. Ten-fold reductions of both copper grain size and carbon particle size were achieved and attributed to the high shear deformation. Energy dispersive X-ray spectrometry indicates the carbon powder was refined to a sub-micron level and uniformly dispersed in the copper matrix. Compared with that of pure copper, the thermal capacity of the composite wire increases by 30 % while density reduces by 29 %.

Friction extrusion, Metal matrix composite, Graphe↗
DOE OSTI2023

Determination of a surrogate for plutonium electrorefining

Conducting research experiments on plutonium electrorefining is difficult due to the significant hazards and regulations associated with nuclear materials. Finding a surrogate for plutonium electrorefining studies would enable more fundamental research to be conducted. Potential surrogates were first identified by determining the physical properties required to conduct electrorefining at the same conditions commonly used in plutonium electrorefining, a molten metal and molten CaCl 2 at 1123 K. Ce-CeCl 3 , In-InCl 3 , and Pb-PbCl 2 were the only potential surrogates identified using these constraints. Sn-SnCl 2 was also tested at these same conditions. More potential surrogates were identified by changing the matrix salt and operating temperature. This expanded the potential surrogate list to also include Zn-ZnCl 2 , Sn-SnCl 2 , and Bi-BiCl 3 . Zn-ZnCl 2 was used with the LiCl-CaCl 2 (65:35 mol%) eutectic at 773 K. Sn-SnCl2 and Bi-BiCl 3 were used with the LiCl-KCl-CaCl 2 (50.5:44.2:5.3 mol%) eutectic at 673–773 K. Ce electrorefining in molten CaCl 2 resulted in a difficult to separate colloid mixture of Ce, Ca and Cl. Electrorefining rates for In in molten CaCl 2 were too slow due to InCl 3 volatilizing out of the molten salt. Only trace amounts of SnCl 2 was retained in the CaCl 2 at 1123 K resulting in impractical electrorefining rates. Zn metal product was successfully collected in the LiCl-CaCl 2 eutectic molten salt, but the metal obtained did not coalesce into one piece. Sn and Bi were successfully electrorefined in the LiCl-KCl-CaCl 2 eutectic molten salt and coalesced into product rings with high yields and coulombic efficiencies. Finally, while a surrogate could not be identified using the same conditions as plutonium electrorefining, two possible surrogates, Sn-SnCl 2 and Bi-BiCl 3 , were found that could imitate the physical configuration (i.e., molten salt on top of molten metal) of plutonium electrorefining at a reduced temperature using the eutectic LiCl-KCl-CaCl 2 salt at 673–773 K in place of CaCl 2 at 1123 K.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Mesoscale Modeling of the Effects of Accelerated Burnup on UO2 Microstructural Evolution

Accelerating the nuclear fuel qualification process will rely on some combination of advanced modeling and simulation techniques with accelerated irradiation testing and separate effects experiments to enable the development of new fuel concepts in a shorter time frame. One of the key challenges to successfully leveraging accelerated irradiation tests will be understanding the artifacts that may be introduced with accelerated accumulation of dose and/or burnup. This work presents phase field (MARMOT) simulations of the evolution of representative 2D UO2 microstructures up to 40 MWd/kgU. Simulations were performed under both commercial light water reactor fuel conditions as well as those that would be expected for highly accelerated (~10x) burnup conditions similar to those used in the MiniFuel irradiations in Oak Ridge National Laboratory’s High Flux Isotope Reactor. The phase field model was coupled with a discrete nucleation algorithm to model re- structuring at high burnup. The effect of the different fission rates in both microstructures was investigated at two temperatures: 650?C and 800?C. The lower temperature simulations both showed an onset of restructuring at nearly 60 MWd/kgU. More extensive restructuring was obtained in the MiniFuel microstructure compared with that of the PWR fuel. At 800?C, no restructuring was obtained as a result of the thermally activated diffusion of Xe atoms and U vacancies to fission gas bubbles, which reduces the nucleation driving force. These results highlight the importance of using modeling and simulation tools to inform the environmental conditions during targeted accelerated irradiation tests to extract the most useful fuel performance data.

accelerated fuel qualification, Phase Field, Restr↗
DOE OSTI2025

UO2 microstructural evolutions induced by Ni, Mo, and W dopants for intentional forensics

The concept of tagging nuclear fuel with a chemical barcode to enable forensics analysis across the nuclear fuel cycle is an area of active investigation, particularly to ensure fabrication viability without disrupting current fuel performance. This study explored the feasibility of using Ni, Mo, and W isotopic double-spikes as dopants in UO2 fuel from the perspective of fuel fabrication. Doped UO2 pellets were produced using conventional fuel fabrication processes, including powder mixing, sieving, pressing, and sintering in a reductive atmosphere. Two composition levels, 100 and 1000 ppm, were evaluated for each dopant element with isotopic double-spike configurations. For the Ni system, additional dopant concentrations of 250 and 500 ppm were produced with nonperturbed isotopic ratios. The results demonstrated that successful incorporation of Ni, Mo, and W double-spikes into UO2 pellets occurred with minimal shift in final density or dopant loss during pellet fabrication. Isotopic analysis confirmed the presence of the double-spike signature even when diluted with natural isotopic material in ratio of 1:5 in the fabrication process. Microstructural examinations revealed different impacts on grain size compared with undoped UO2. This study showed that Ni incorporation up to ∼500 ppm promoted moderate grain growth, whereas the Mo and W systems caused grain size reduction at all concentrations. Changes in the UO2 lattice parameter as a function of composition were detected exclusively for Ni up to 500 ppm, indicating that the Ni solid solution was the main factor for the observed grain growth. Insoluble (Mo and W) or supersaturated (Ni > 500 ppm) conditions produced grain size reduction. The Ni-doped pellets in the solution range resulted in a final microstructure within fuel specifications, demonstrating its potential benefits of employing complex dopant systems for potential nuclear forensic applications.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Optimal sensor placement for reconstructing wind pressure field around buildings using compressed sensing

Deciding how to optimally deploy sensors in a large, complex, and spatially extended structure is critical to ensure that the surface pressure field is accurately captured for subsequent analysis and design. In some cases, reconstruction of missing data is required in downstream tasks such as the development of digital twins. Here, this paper presents a data-driven sparse sensor selection algorithm, aiming to provide the most information contents for reconstructing aerodynamic characteristics of wind pressures over tall building structures parsimoniously. The algorithm first fits a set of basis functions to the training data, then applies a computationally efficient QR algorithm that ranks existing pressure sensors in order of importance based on the state reconstruction to this tailored basis. The findings of this study show that the proposed algorithm successfully re- constructs the aerodynamic characteristics of tall buildings from sparse measurement locations, generating stable and optimal solutions across a range of conditions. As a result, this study serves as a promising first step toward leveraging the success of data-driven and machine learning algorithms to supplement traditional genetic algorithms currently used in wind engineering.

42 ENGINEERING↗
DOE OSTI2024

A novel transient infrared imaging method for non-intrusive, low-cost, fast, and accurate air leakage detection in building envelopes

Air leakage through the building envelope in the U.S. accounts for about four quads of energy annually, costing approximately $40 billion per year. However, a high-fidelity and non-intrusive method to detect air leakage has not been demonstrated to date. Here, in this paper, we propose a novel non-intrusive and low-cost method called Transient Infrared (IR) Imaging (TIRI) that can rapidly and accurately identify air leakage locations and relative rates on building envelopes. When the interior and exterior temperatures are different, and a small internal pressure pulse is created by HVAC, the temperature at locations with air leakages will change rapidly, while the areas without a leakage do not change. Based on a heat transfer model, we have derived the temperature change as a function of time after the HVAC is turned on. By tracking the temperature change, which depends on leakage rate and size, we have obtained the air leakage map in the case studies. Using an exterior door as an example, we took transient IR images in different seasons and different times of the day, and successfully obtained the leakage map in all the scenarios. Successfully obtained the air leakage map even when the indoor-outdoor air temperature difference is as small as 2 °C. We have also realized a detection speed of 10s and demonstrated that this method also worked for windows, which have mirror-like IR reflections. Our TIRI method will accelerate the improvement of airtightness in buildings, save building energy, and help reduce greenhouse gas emissions.

42 ENGINEERING↗
DOE OSTI2022

Machine learning with knowledge constraints for process optimization of open-air perovskite solar cell manufacturing

Perovskite photovoltaics (PV) have achieved rapid development in the past decade in terms of power conversion efficiency of small-area lab-scale devices; however, successful commercialization still requires further development of low-cost, scalable, and high-throughput manufacturing techniques. One of the critical challenges of developing a new fabrication technique is the high-dimensional parameter space for optimization, but machine learning (ML) can readily be used to accelerate perovskite PV scaling. Herein, we present an ML-guided framework of sequential learning for manufacturing process optimization. We apply our methodology to the Rapid Spray Plasma Processing (RSPP) technique for perovskite thin films in ambient conditions. With a limited experimental budget of screening 100 process conditions, we demonstrated an efficiency improvement to 18.5% as the best-in-our-lab device fabricated by RSPP, and we also experimentally found 10 unique process conditions to produce the top-performing devices of more than 17% efficiency, which is 5 times higher rate of success than the control experiments with pseudo-random Latin hypercube sampling. Our model is enabled by three innovations: (a) flexible knowledge transfer between experimental processes by incorporating data from prior experimental data as a probabilistic constraint; (b) incorporation of both subjective human observations and ML insights when selecting next experiments; (c) adaptive strategy of locating the region of interest using Bayesian optimization first, and then conducting local exploration for high-efficiency devices. Furthermore, in virtual benchmarking, our framework achieves faster improvements with limited experimental budgets than traditional design-of-experiments methods (e.g., one-variable-at-a-time sampling). This framework shows the capability of incorporating researchers’ domain knowledge into the ML-guided optimization loop; therefore, it has the potential to facilitate the wider adoption of ML in scaling to perovskite PV manufacturing.

14 SOLAR ENERGY↗
DOE OSTI2026

Tetrahedral Zn 2+ doping LiNi 0.6 Mn 0.2 Co 0.2 O 2 improves discharge capacity retention by altering surface Ni valence during cycling and preventing oxygen evolution

5 mol-% Zn 2+ was added to the co-precipitation synthesis of LiNi 0.6 Mn 0.2 Co 0.2 O 2 (NMC622) to understand Zn's effect on the structural and electronic properties of NMC622. Zn is determined to successfully dope into the NMC crystal lattice as there is an increase in d-spacing along the [003], [104], and [110] directions and an increase in molar volume (100.75 cm 3 mol -1 for NMC622 and 101.00 cm 3 mol -1 for Zn-NMC). Through XPS, Zn is determined to dope on the tetrahedral 6c Wyckoff site in the NMC structure and TEM-EDS reveals that despite a slight ZnO impurity phase, 1.4 mol-% Zn successfully dopes into the crystal structure. Furthermore, cyclic voltammetry shows a suppressed anodic wave associated with oxygen evolution and EIS demonstrates that Zn-doped NMC decreases charge transfer resistance and increases Li diffusion by an order of magnitude. After the formation cycles, NMC has a higher 1 C accessible capacity, 141 mAh/g, over Zn-NMC (132 mAh/g), but after long term cycling Zn-NMC has a higher accessible capacity (117 mAh/g compared to 96 mAh/g for NMC) and increases discharge capacity retention by 18% leading to longer cycle life.

Electrochemistry↗
DOE OSTI2025

Microstructure and CPO evolution of dynamically recrystallized olivine during complex deformation conditions: a full-field numerical modeling approach

The rheological properties of mantle rocks are strongly dependent on their crystallographic preferred orientation (CPO). Olivine CPO, defined by the orientation of seismically fast [100] axes parallel to flow direction, is also thought to be a dominant contributor to seismic anisotropy in the Earth's upper mantle. However, the amount of deformation needed to overprint a new CPO on a pre-existing fabric and the impact of the inherited CPOs on the transient microstructure evolution, remain unknown. This study employs a full-field numerical approach (VPFFT-ELLE) to explore the dynamic recrystallization and microstructural evolution of olivine polycrystalline aggregates under complex deformation conditions. We test four combinations of successive pure shear and simple shear boundary conditions. Findings indicate that inherited CPOs influence subsequent deformation in a manner dependent on the kinematic relationship between successive stages. In all cases, a minor strain increment (ε ∼0.3–0.6) is sufficient to erase the previous microstructure and CPO. However, when deformation conditions change dramatically (e.g., stretching direction changes orthogonally), the intensity of the new CPO developed is significantly lower and strain distribution are specially altered. During a transient strain stage, pre-existing microstructures undergo extensive reworking, especially when deformation conditions are changed dramatically, such as switching from simple shear to a pure shear condition with a parallel shortening direction relative to the stretching direction. We estimate the significance of these results in interpreting observations of seismic velocity anisotropy, concluding that P-wave seismic anisotropy is significantly and positively correlated with the evolution of olivine CPO with deformation history. This research underscores the transient nature of microstructural rearrangement in olivine aggregates and the necessity for caution in interpreting seismic anisotropy in regions with complex deformation histories, as inherited CPOs can influence current fabric development and induce deviation to the present deformation conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Controlled dehumidification to extract clean water from a multicomponent gaseous mixture of organic contaminants

Rapid expansion of the unconventional oil and gas extraction has increased American energy independence, but also led to increased production of large amounts of contaminated water. Wastewater from the oil and gas industry contains a wide range of contaminants. Injecting such contaminated water into disposal wells or discharging it to the environment jeopardizes freshwater resources. Conventional and membrane-based wastewater treatment techniques are often not effective choices to treat highly contaminated wastewater; however, a suitable way to remove contaminants from wastewater is to selectively separate water in a process analogous to humidification-dehumidification (HDH). Only a few studies have investigated the use of HDH for wastewater treatment. In this study, a novel HDH system is introduced for treating highly contaminated water, such as oil and gas flowback and produced water. In this HDH process, a non-condensable gas, such as air, mixes with wastewater vapor to facilitate the separation of contaminants. As a result, clean water condenses from the multicomponent gaseous mixture while air carries organic contaminants out of the dehumidification section. A laboratory apparatus was constructed and experiments were performed to investigate the HDH process for wastewater treatment and to study the effects of flow dynamics including air flow rate on the composition of treated water. Different contaminants including benzene, toluene, 2-propanol and 2-butoxyethanol were tested. Experimental results showed that the system can be successfully applied for removing volatile and/or toxic organic contaminants from wastewater. A representative multicomponent mixture of fracking wastewater was successfully treated using the experimental setup and clean water with quality of 98.3% was obtained. It was revealed that increasing the air-to-vapor mass ratio improves purity of treated water. Quantitative analysis showed that by increasing the air-to-vapor mass ratio from 0.6 to 5.9, the fraction of separated 2-propanol through the air was improved from 43% to more than 96% of the initial amount. ASPEN software was employed to simulate equilibrium conditions. Finally, experimental results were observed to have lower mass fraction of residual contaminant in the treated water compared to equilibrium state.

42 ENGINEERING↗