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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Physical mechanisms for multiphase flow associated with hydrate formation

Many Arctic hydrate reservoirs such as those of the Prudhoe Bay and Kuparuk River area on the Alaska North Slope (ANS) are believed originally to be natural gas accumulations converted to hydrate accumulations after being placed in the gas hydrate stability zone (GHSZ) in response to ancient climate cooling. In this paper, the implications of a previously described mechanistic model for the transport of gaseous and aqueous phases are studied using a transient 1-D transport model during the conversion of a gas reservoir to a hydrate reservoir. The mechanistic model predicts/explains the vertical profile of hydrate saturation in “converted free gas” hydrate reservoirs. The initial gas phase saturation with depth is estimated from the profile of capillary entry pressure, which is estimated from grain size distributions measured in cores. The gas accumulation is assumed to be disconnected from its original source so that methane transport occurs only within it. As the base of the GHSZ descends through the sediment, hydrate forms within the GHSZ. The net volume reduction associated with hydrate formation creates a “sink” which drives flow of gaseous and aqueous phases to the hydrate formation zone. Mechanisms by which this fluid movement could have occurred are analyzed. Flow driven by saturation gradients plays a key role in creating reservoirs of large hydrate saturations, as observed in Mount Elbert stratigraphic test well in the Milne Point Unit of Alaska North Slope (ANS). Viscous-dominated pressure-driven flow of gaseous and aqueous phases cannot explain large hydrate saturations originated from large-saturation gas accumulations. The mode of hydrate formation for a wide range of rate of hydrate formation, the rate of descent of the base of GHSZ, and host sediment characteristics are analyzed and characterized based on dimensionless groups. The proposed transport model is also consistent with field data from hydrate-bearing sand units in Mount Elbert well. Finally, results show that not only the petrophysical properties of the host sediment but also the rate of hydrate formation and the rate of temperature cooling at the surface contribute greatly to the final hydrate saturation profiles.

58 GEOSCIENCES↗
DOE OSTI2017

Micro-PIV measurements of multiphase flow of water and liquid CO 2 in 2-D heterogeneous porous micromodels

We present an experimental study of pore-scale flow dynamics of liquid CO 2 and water in a two-dimensional heterogeneous porous micromodel, inspired by the structure of a reservoir rock, at reservoir-relevant conditions (80 bar, 21°C). The entire process of CO 2 infiltration into a water-saturated micromodel was captured using fluorescence microscopy and the micro-PIV method, which together reveal complex fluid displacement patterns and abrupt changes in velocity. The CO 2 front migrated through the resident water in an intermittent manner, forming dendritic structures, termed fingers, in directions along, normal to, and even opposing the bulk pressure gradient. Such characteristics indicate the dominance of capillary fingering through the micromodel. Velocity burst events, termed Haines jumps, were also captured in the heterogeneous micromodel, during which the local Reynolds number was estimated to be ~21 in the CO 2 phase, exceeding the range of validity of Darcy's law. Furthermore, these drainage events were observed to be cooperative (i.e., across multiple pores simultaneously), with the zone of influence of such events extending beyond tens of pores, confirming, in a quantitative manner, that Haines jumps are nonlocal phenomena. After CO 2 completely breaks through the porous section, shear-induced circulations caused by flowing CO 2 were also observed, in agreement with previous studies using a homogeneous porous micromodel. To our knowledge, this study is the first quantitative measurement that incorporates both reservoir-relevant conditions and rock-inspired heterogeneity, and thus will be useful for pore-scale model development and validation.

42 ENGINEERING↗
DOE OSTI2024

Digital Multiphase Composites via Additive Manufacturing

Abstract Mechanical properties of traditional engineering materials are typically coupled to each other, presenting a challenge to practitioners with multi‐dimensional material property requirements. In this work, continuous, independent control over multiple mechanical properties is demonstrated in composite materials realized using additive manufacturing. For the first time, composites additively manufactured from rigid plastic, soft elastomer, and liquid constituents are experimentally characterized, demonstrating materials which span four orders of magnitude in modulus and two orders of magnitude in toughness. By forming analytical mappings between relative concentrations of constituents at the microscale and resulting macroscale material properties, inverse material design is enabled; the method is showcased by printing artifacts with prescribed toughness and elasticity distributions. The properties of these composites are placed in the context of biological tissues, showing they have promise as mechanically plausible tissue mimics.

Smith, Lawrence T.↗
DOE OSTI2015

Multiphase Nanostructure of a Quinary Metal Oxide Electrocatalyst Reveals a New Direction for OER Electrocatalyst Design

Ce-rich mixed metal oxides comprise a recently discovered class of ­electrocatalysts for the oxygen evolution reaction (OER). In particular, at current densities below 10 mA cm -2 , Ni 0.3 Fe 0.07 Co 0.2 Ce 0.43 O x exhibits ­superior activity compared to the corresponding transition metal oxides, despite the relative inactivity of ceria. To elucidate the enhanced activity and underlying catalytic mechanism, detailed structural characterization of this quinary oxide electrocatalyst is reported in this paper. Transmission electron microscopy imaging of cross-section films as-prepared and after electrochemical testing reveals a stable two-phase nanostructure composed of 3–5 nm diameter crystallites of fluorite CeO 2 intimately mixed with 3–5 nm crystallites of transition metal oxides alloyed in the rock salt NiO structure. Dosing experiments demonstrate that an electron flux greater than ≈1000 e Å -2 s -1 causes the inherently crystalline material to become amorphous. A very low dose rate of 130 e Å -2 s -1 is employed for atomic resolution imaging using inline holography techniques to reveal a nanostructure in which the transition metal oxide nanocrystals form atomically sharp boundaries with the ceria nanocrystals, and these results are corroborated with extensive synchrotron X-ray absorption spectroscopy measurements. Finally, ceria is a well-studied cocatalyst for other heterogeneous and electrochemical reactions, and our discovery introduces biphasic cocatalysis as a design concept for improved OER electrocatalysts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Deep learning and radiomics: the utility of Google TensorFlow™ Inception in classifying clear cell renal cell carcinoma and oncocytoma on multiphasic CT

Purpose: Currently, all solid enhancing renal masses without microscopic fat are considered malignant until proven otherwise and there is substantial overlap in the imaging findings of benign and malignant renal masses, particularly between clear cell RCC (ccRCC) and benign oncocytoma (ONC). Radiomics has attracted increased attention for its utility in pre-operative work-up on routine clinical images. Radiomics based approaches have converted medical images into mineable data and identified prognostic imaging signatures that machine learning algorithms can use to construct predictive models by learning the decision boundaries of the underlying data distribution. The TensorFlow™ framework from Google is a state-of-the-art open-source software library that can be used for training deep learning neural networks for performing machine learning tasks. The purpose of this study was to investigate the diagnostic value and feasibility of a deep learning-based renal lesion classifier using open-source Google TensorFlow™ Inception in differentiating ccRCC from ONC on routine four-phase MDCT in patients with pathologically confirmed renal masses. Methods: With institutional review board approval for this 1996 Health Insurance Portability and Accountability Act compliant retrospective study and a waiver of informed consent, we queried our institution’s pathology, clinical, and radiology databases for histologically proven cases of ccRCC and ONC obtained between January 2000 and January 2016 scanned with a an intravenous contrast-enhanced four-phase renal mass protocol (unenhanced (UN), corticomedullary (CM), nephrographic (NP), and excretory (EX) phases). To extract features to be used for the machine learning model, the entire renal mass was contoured in the axial plane in each of the four phases, resulting in a 3D volume of interest (VOI) representative of the entire renal mass. We investigated thirteen different approaches to convert the acquired VOI data into a set of images that adequately represented each tumor which was used to train the final layer of the neural network model. Training was performed over 4000 iterations. In each iteration, 90% of the data were designated as training data and the remaining 10% served as validation data and a leave-one-out cross-validation scheme was implemented. Accuracy, sensitivity, specificity, positive (PPV) and negative predictive (NPV) values, and CIs were calculated for the classification of the thirteen processing modes. Results: We analyzed 179 consecutive patients with 179 lesions (128 ccRCC and 51 ONC). The ccRCC cohort had a mean size of 3.8 cm (range 0.8–14.6 cm) and the ONC cohort had a mean lesion size of 3.9 cm (range 1.0–13.1 cm). The highest specificity and PPV (52.9% and 80.3%, respectively) were achieved in the EX phase when we analyzed the single mid-slice of the tumor in the axial, coronal and sagittal plane, and when we increased the number of mid-slices of the tumor to three, with an accuracy of 75.4%, which also increased the sensitivity to 88.3% and the PPV to 79.6%. Using the entire tumor volume also showed that classification performance was best in the EX phase with an accuracy of 74.4%, a sensitivity of 85.8% and a PPV of 80.1%. When the entire tumor volume, plus mid-slices from all phases and all planes presented as tiled images, were submitted to the final layer of the neural network we achieved a PPV of 82.5%. Conclusions: The best classification result was obtained in the EX phase among the thirteen classification methods tested. Our proof of concept study is the first step towards understanding the utility of machine learning in the differentiation of ccRCC from ONC on routine CT images. We hope this could lead to future investigation into the development of a multivariate machine learning model which may augment our ability to accurately predict renal lesion histology on imaging.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

Determining Individual Phase Flow Properties in a Quench and Partitioning Steel with In Situ High-Energy X-Ray Diffraction and Multiphase Elasto-Plastic Self-Consistent Method

The micromechanical properties of the constituent phases were characterized for an advanced high strength steels (AHSS) produced by a quenching and partitioning (Q&P) process with in-situ tensile loading under Synchrotron-based high energy X-ray diffraction. The constituent phases present are retained austenite and three martensites (tempered, untempered and freshly formed martensites). For the material investigated, the 200 and 220 lattice strains of the retained austenite phase were calculated by examining the changes of the X-ray diffraction peak positions during deformation. The 200 and 211 lattice strains of the various martensitic phases with similar crystal structures were determined by separating their overlapped diffraction peaks. Apart from tempered and untempered martensite, the diffraction peaks of freshly formed martensite as a result of austenite to martensite transformation can also be able separated due to high initial austenite volume fraction. The phase stresses are first estimated with an empirical relationship through the X-ray diffraction elastic constants (XREC). A multi-phase elasto-plastic self constant model (EPSC) is next used for more accurate determination of the constitutive behaviors of the various phases by comparing the predicted lattice strain distributions and global stress-strain curves with the measured ones. The determined constitutive laws will be used for microstructure-based modeling for sheet formability of the Q&P AHSS steel.

Hu, Xiaohua↗
DOE OSTI2019

Microstructure and Mechanical Properties of a Multiphase FeCrCuMnNi High-Entropy Alloy

A FeCrCuMnNi high-entropy alloy was produced using vacuum induction melting, starting from high-purity raw materials. The microstructure and mechanical properties of the as-cast FeCrCuMnNi alloy were studied, considering x-ray diffraction (XRD), scanning electron microscopy, and hardness and tensile tests. XRD results revealed the existence of two FCC phases and one BCC phase. Microstructural evaluation illustrated that the as-cast alloy has a typical cast dendritic structure, where dendrite regions (BCC) were enriched in Cr and Fe. Interdendritic regions were saturated with Cu and Ni and revealed G/B(T) {110} 〈111〉 and Brass {110} 〈112〉 as the major texture components. The produced alloy revealed an excellent compromise in mechanical properties due to the mixture of solid solution phases with different structures: 300 HV hardness, 950 MPa ultimate tensile strength and 14% elongation. Microhardness test results also revealed that the BCC phase was the hardest phase. The fracture surface evidenced a typical ductile failure. Furthermore, heat treatment results revealed that phase composition remained stable after annealing up to 650 °C. Phase transformation occurred at higher temperatures in order to form more stable phases; therefore, FCC2 phase grew at the expense of the BCC phase.

36 MATERIALS SCIENCE↗